Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation Enhui Chai1
Sicheng Chen2 Tianyi Zhang3 Chad Wong2 Zeyu Liu1 Fei Xia2
Kecheng Huang1
1
2
PuzzleLogic Pte Ltd, Singapore 229594, Singapore Nhu Department of Electrical Engineering and Computer Science, University of California, Irvine 3 Department of Electrical & Computer Engineering, National University of Singapore
arXiv:2605.05164v1 [cs.CV] 6 May 2026
Corresponding author: Zeyu Liu<[email protected]>, Fei Xia<[email protected]>
Abstract Accurate analysis of histopathological images is critical for disease diagnosis and treatment planning. Wholeslide images (WSIs), which digitize tissue specimens at gigapixel resolution, are fundamental to this process but require aggregating thousands of patches for slide-level predictions. Multiple Instance Learning (MIL) tackles this challenge with a two-stage paradigm, decoupling tile-level embedding and slide-level prediction. However, most existing methods implicitly embed patch representations in homogeneous Euclidean spaces, overlooking the hierarchical organization and regional heterogeneity of pathological tissues. This limits current models’ ability to capture global tissue architecture and fine-grained cellular morphology. To address this limitation, we introduce a hybrid hyperbolic-Euclidean representation that embeds WSI features in dual geometric spaces, enabling complementary modeling of hierarchical tissue structures and local morphological details. Building on this formulation, we develop BatMIL, a WSI classification framework that leverages both geometric spaces. To model long-range dependencies among thousands of patches, we employ a structured state space sequence model (S4) backbone that encodes patch sequences with linear computational complexity. Furthermore, to account for regional heterogeneity, we introduce a chunk-level mixture-of-experts (MoE) module that groups patches into regions and dynamically routes them to specialized subnetworks, improving representational capacity while reducing redundant computation. Extensive experiments on seven WSI datasets spanning six cancer types demonstrate that BatMIL consistently outperforms state-of-the-art MIL approaches in slide-level classification tasks. These results indicate that geometry-aware representation learning offers a promising direction for next-generation computational pathology. Keywords Computational pathology · whole-slide images · multiple instance learning · hybrid geometric embedding
1
Introduction
Histopathological examination is the gold standard for disease diagnosis, forming the foundation for accurate cancer diagnosis and treatment planning [1]. Whole-slide images (WSIs) are central to computational pathology, yet present a critical challenge due to their gigapixel resolution (e.g., 30, 000 × 50, 000 Raw Image (a) WSI Euclidean Distance‑Based BatMIL Heatmap pixels) poses significant computational challenges. To tackle this gigapixel challenge, Multiple Instance Learning (MIL) [2] an emerging dominant solution by decoupling the encoding and decoding processes. It partitions WSIs into small regions (e.g., 224 × 224 pixels), generating patch embeddings via foundation models (e.g., UNI [3], Virchow2 [4]), and aggregates them via slide-level models (e.g., ABMIL [5], TransMIL [6]). Although Ground Truth (b) WSI Hyperbolic Distance‑Based BatMIL Heatmap practical, previous MIL methods primarily focus on the resolution issue, leaving the intrinsic biological properties of WSIs insufficiently addressed. Specifically, they suffer from three Figure 1: WSI geometric representations. (a) Euclidean distance fails to model hierarchical relationships. (b) Hyperbolic geomefollowing key limitations: try naturally encodes the hierarchical structure of pathology. Geometric trade-offs in modeling multi-level tissue hierarchies. Beyond spatial dependencies and heterogeneity, pathological tissues inherently exhibit a multi-level hierarchical architecture (e.g., cells → glands → tissues). Most traditional MIL methods gles to embed exponential hierarchical growth. Consequently, rely on Euclidean distance, which excels at capturing locally Euclidean-based activation maps often scatter attention across continuous morphological features but mathematically strug- diagnostic-irrelevant background or stromal regions (Fig. 1 (a)).
Preprint – Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation To address this, recent pioneering works [7] have introduced hyperbolic geometry to WSI analysis, exploiting its inherent capacity to embed hierarchical topologies. However, pure hyperbolic representations face a fundamental geometric trade-off: their inherent negative curvature distorts fine-grained local morphological details that are naturally better preserved in flat Euclidean spaces. Therefore, a hybrid geometry is required to harmonize these spaces, precisely concentrating heatmap activations on tumor boundaries while suppressing irrelevant background noise (Fig. 1 (b)). Inadequate modeling of long-range 2D spatial dependencies. Effectively capturing global tissue structures requires modeling inter-tile relationships across thousands of patches, which State Space Models (SSMs) excel at (Fig. 2 (a)). Among them, the Structured State Space Sequence (S4) model [8] is an early and representative formulation that offers stable long-context modeling through structured state-space parameterization. Later selective SSMs such as Mamba further improve sequence modeling efficiency and adaptivity, but their selective scanning mechanism still imposes an ordered 1D traversal over spatial tokens, which can introduce directional bias and structurally conflicts with the non-directional 2D grid topology of WSIs (Fig. 2 (c)). In contrast, S4 provides a more suitable basis for our setting: its structured global recurrence can model long-range interactions without relying on rigid scan directions, thereby better aligning with the complex 2D spatial organization of pathological tissues while maintaining linear complexity and stable global receptive fields (Fig. 2 (b)). Vulnerability to pronounced regional heterogeneity. A single WSI is a highly heterogeneous microenvironment composed of morphologically distinct regions, such as dense tumor areas, necrotic zones, and normal stroma. Traditional MIL methods attempt to fit these divergent distributions using a single unified network backbone. This often leads to parameter dilution and task interference, resulting in strong performance in dominant regions but severe bias or underfitting in others [9]. Effectively disentangling these highly varied histological features remains a significant bottleneck, necessitating a dynamic routing mechanism that can adaptively assign specialized computational resources to distinct morphological patterns. Building upon these insights, we propose BatMIL (Fig. 3), a novel MIL framework that conceptually bridges S4, MoE, and geometric representation learning to systematically address the aforementioned limitations. Specifically, we design a cascaded backbone network that utilizes S4 as the core for unbiased 2D global context modeling, while introducing a mixture-ofexperts (MoE) module to dynamically route heterogeneous regional features. Furthermore, we develop a hybrid geometric representation module to jointly embed WSI features into a hyperbolic-Euclidean space, fully encapsulating the hierarchical tissue topology without sacrificing local details. Our main contributions are summarized as follows: • We propose BatMIL, a novel geometry-aware MIL framework that fundamentally addresses the challenges of ultra-long spatial modeling, regional heterogeneity, and hierarchical tissue topologies in WSI analysis. • We develop a novel cascaded S4-MoE backbone (Fig. 2 (d)). The S4 module acts as an unbiased engine for
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capturing global 2D context with linear complexity, while the pioneering integration of the MoE module dynamically routes heterogeneous tissue patches to specialized experts, mitigating feature homogenization. • We design a hybrid hyperbolic-Euclidean representation module. By harmonizing the hierarchical embedding capacity of hyperbolic geometry with the local morphological sensitivity of Euclidean space, this dualspace approach significantly concentrates diagnostic attention and suppresses irrelevant background noise. • Experiments on seven WSI datasets spanning diverse cancer types demonstrate that BatMIL outperforms thirteen state-of-the-art MIL methods across multiple downstream tasks, establishing a new paradigm for structured representation in computational pathology.
2
Related Work
2.1
Multiple Instance Learning
Deep learning for WSI analysis has evolved toward a twostage MIL paradigm, separating tile-level embedding and slidelevel modeling. ABMIL [5] addresses it by implementing a gated attention mechanism to adaptively weight informative tiles. Building on this, CLAM [10] imposes instance-clustering constraints to encourage diverse and discriminative feature learning, thereby improving model generalization. Furthermore, DSMIL [11] incorporates contrastive learning by combining instance- and bag-level supervision to better distinguish informative tiles. On the other hand, DTFD-MIL [12] proposes a double-tier feature distillation framework that utilizes pseudobags to virtually expand the training set, enhancing robustness in small-sample scenarios. However, traditional MIL methods often assume instances are independent, failing to capture the spatial and morphological inter-tile relationships. To address this, graph-based and multiscale hierarchical methods were introduced. Patch-GCN [13] hierarchically aggregates instance-level histology features via a Graph Convolutional Network (GCN) to model local and global topological structures. Similarly, ZoomMIL [14] builds WSI representations by aggregating tissue-context information from multiple magnifications, while HiGT [15] introduces a hierarchical graph transformer to learn short-range local details and long-range global patterns. Despite successfully capturing spatial contexts, these methods rely heavily on explicitly defined graph topologies or rigid scales, limiting their flexibility. To model global patch correlations without manual topological priors, Transformer-based methods have been widely adopted. TransMIL [6] overcomes the instance independence challenge by introducing a Transformer-based architecture, utilizing the Nyström approximation of the self-attention mechanism to explicitly model these correlations with pseudo-linear complexity. However, for the extreme sequence lengths inherent in gigapixel WSIs, such approximations still face memory bottlenecks and degradation in capturing fine-grained long-range dependencies. To explicitly improve long-context understanding, GigaPath [16] adopts a cascaded structure and leverages the LongNet architecture. While its dilated attention effectively scales to extreme sequence lengths, the massive parameter count
Preprint – Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation
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Figure 2: Comparison of SSM structures. (a) SSM (State Space Model), (b) S4 (Structured State Space Sequence Model), (c) Mamba (Selective State Space Model), (d) S4-MoE (Ours). of GigaPath imposes a severe computational hardware burden the pronounced regional heterogeneity of WSIs, allowing morcompared to lightweight aggregators. phologically distinct tissue patches to be adaptively processed by specialized experts rather than a constrained unified backbone. Recently, State Space Models (SSMs) have emerged as a promising paradigm to break the computational bottlenecks of attention mechanisms while maintaining effective global receptive 2.3 Hyperbolic Space fields. Leveraging true linear complexity, S4MIL [17] introRecent advances in WSI analysis have increasingly recogduces the structured state space sequence (S4) model to WSI nized the geometric limitations of traditional Euclidean spaces, analysis, providing robust long-range dependency modeling. prompting the exploration of hyperbolic neural networks. To Subsequent works, such as MambaMIL [18] and MamMIL [19], better capture the intrinsic hierarchical organization of tissue adapt the Mamba architecture to capture inter-tile dependenstructures, methods such as HyperPath [7] and HVHM [26] have cies efficiently through selective scanning mechanisms, while embedded WSI features into hyperbolic spaces. By exploiting PathRWKV [20] explores linear recurrent neural networks for the exponential volume growth inherent to hyperbolic geometry, scalable WSI modeling. these approaches explicitly model tree-like tissue hierarchies However, all of these previous methods solely embedded with significantly lower distortion than early Euclidean-based their feature representations in Euclidean space. This geometric MIL methods. However, these pure hyperbolic representations constraint prevents them from explicitly capturing the intrinsic face a fundamental geometric trade-off [27, 28]. While hypertree-like hierarchical topology of pathological structures. Fur- bolic space excels at capturing global topological hierarchies, thermore, these unified network architectures lack the special- its inherent curvature often distorts fine-grained local and disized capacity to dynamically adapt to the pronounced regional criminative morphological features—attributes that are naturally heterogeneity present in WSIs. better preserved in flat Euclidean spaces. Consequently, effectively harmonizing these two geometries to jointly model global hierarchical structures while preserving local feature discrim2.2 Mixture of Experts in Transformers inability remains a critical open challenge, directly motivating Mixture of Experts (MoE) provides an elegant paradigm the hybrid geometric representation proposed in our work. for efficient model scaling by decoupling computational cost from total parameter count [21]. In Transformer architectures, this is achieved by replacing standard dense Feed-Forward Net- 3 Methods works (FFNs) with a sparse ensemble of parallel "expert" subnetworks. For each input token, a lightweight gating network 3.1 WSI Pre-processing and Embedding (router) dynamically activates only a sparse subset of these exTo enable effective feature extraction and subsequent slideperts. Following its initial integration into Transformers via level analysis on WSIs, we adopted a systematic preprocessing GShard [22], subsequent architectures like the Switch Transpipeline to filter uninformative regions and generate high-quality former [23] streamlined the paradigm using a top-1 routing tiles. For a given WSI, we first load it at 0.5 microns-per-pixel mechanism, coupled with auxiliary load-balancing losses to mit(mpp) resolution. To avoid redundant computation on the backigate expert collapse. More recently, models such as Mixtral ground, we compute the luminance of the slide and apply Otsu’s 8x7B [24] have demonstrated the robust scalability of top-k thresholding to generate a binary foreground mask. The slide routing, achieving state-of-the-art performance. Beyond natuis then cropped into bounding boxes encompassing the tissue ral language processing, Vision MoE (V-MoE) [25] extended regions. this architecture to computer vision by routing individual image patches. In the context of computational pathology, this dyAs shown in Fig. 3(a), these bounding boxes are split into a namic patch-level routing offers a highly promising solution to grid of non-overlapping tiles, each with a size of 224 × 224. The
Preprint – Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation AJCC HDE
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Figure 3: Overview of BatMIL. (a) A WSI is first partitioned into tiles, then encoded into embeddings with a pathological foundation model. (b) S4-MoE employs WSI-specialized experts to extract domain-specific features. (c) The HDE module maps features from Euclidean space into a hyperbolic manifold to capture hierarchical relationships. (d) The GHS module integrates embeddings from both hyperbolic and Euclidean geometries to generate a multi-scale representation. WSI can then be represented as a set of tiles {Pi, j }. To ensure these samples are useful for pathological analysis, we adopt a rigorous screening process to exclude low-quality samples. First, we discard any tile where the tissue occupancy is below 10%. Second, we eliminate tiles with insufficient pixel variation by computing the standard deviation across the RGB channels; tiles with an average standard deviation of less than 5 on a 0–255 scale are removed. Additionally, we filter out anomalous tiles containing excessive pure background, specifically discarding those where more than 50% of the pixels have zero intensity.
3.3
To effectively capture global tissue structures without the quadratic computational bottleneck of standard Transformers or the 1D spatial bias of Mamba, we employ the Structured State Space Sequence (S4) model as the core of our cascaded backbone. The S4 module is designed to jointly model global context and local details across tens of thousands of patches with linear complexity.
where I is the set of selected expert indices, w j is the gating weight, and E j represents the j-th expert. Through this dynamic sparse activation, computational resources are adaptively assigned to morphologically distinct regions.
MoE Routing
WSIs are highly heterogeneous microenvironments comprising distinct regions. Traditional unified networks often suffer from parameter dilution when fitting these divergent distributions. To address this pronounced regional heterogeneity, the updated features from the S4 branch are fed into a sparse MoE module. We instantiate k=4 expert sub-networks, each formulated as a two-layer feedforward network with GELU activation and dropout to enhance non-linear expressiveness. Instead of routing all patches through the same dense layers, a lightweight The remaining tiles contain meaningful pathological infor- gating network dynamically selects the top-2 specialized experts mation, and we subsequently utilize the tile-level encoder from for each individual patch. The gating network computes logits GigaPath [16] to convert them into compact semantic feature that are normalized via softmax to yield routing weights w j . The vectors. This feature extraction step improves both the training outputs of the selected experts are then aggregated and residually efficiency of the downstream slide-level backbone and its overall fused: performance in WSI-level analysis. X x MoE = xS 4 + DropPath w j · E j (LN(xS 4 )) (2) 3.2 Structured State Space Sequence Module j∈I
Following the cascaded S4-MoE backbone, we apply global max-pooling along the patch sequence dimension to aggregate the patch-level features into a unified slide-level repreFor an input sequence of patch embeddings x, we first ap- sentation, preserving the most discriminative spatial responses: ply layer normalization (LN) to stabilize the distribution. The xslide = MaxPool(x MoE , dim = 1) (3) features are then processed by the S4 transformation, which leverages HiPPO (High-order Polynomial Projection Operators) The slide-level feature xslide is then repeated to match the token initialization. This enables the stable retention of long-range count required for subsequent multi-task learning, yielding the memory and inherently aligns with the non-directional 2D spafinal output tensor used for task-specific predictions. tial topology of WSIs. To facilitate feature reuse and ensure stable gradient flow in deep networks, we introduce an element3.4 Hybrid Geometric Representation wise residual connection via DropPath: xS 4 = x + DropPath (S4 (LN(x))) (1) While the S4-MoE backbone effectively extracts slide-level This unbiased processing empowers the S4 branch to construct representations, conventional MIL methods embed these feaa robust global receptive field while preserving local morpho- tures exclusively in Euclidean space. Euclidean geometry natulogical details. rally preserves fine-grained local morphological details via the
Preprint – Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation
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L2 norm, but mathematically struggles to model the exponen- Table 1: Implemented datasets and corresponding downstream tial tree-like hierarchical architecture intrinsic to pathological tasks. tissues. To capture this broader hierarchical scale, we introduce Dataset Number of WSIs Task Organ hyperbolic geometry via the Poincaré ball model Mnc , governed CAMELYON16 400 BrMet Lymph Node by the manifold curvature c. Its distance metric Dhyp is derived from Möbius addition (⊕c ): CAMELYON17 500 BrMet Lymph Node √ 2 PANDA 10616 G-Score Prostate Dhyp (x, y) = √ arctanh c − x ⊕c y (4) c TCGA-BLCA 926 GRADE Bladder This manifold embedding scales exponentially with the radius, TCGA-BRCA 3121 IHC-HER2 Breast effectively accommodating hierarchical tissue structures with TCGA-CESC 604 LymInv Cervix minimal distortion. However, this global advantage introduces TCGA-NSCLC 3210 T-Stage Lung a geometric trade-off: pure hyperbolic space’s inherent negative curvature can distort local discriminative features that are otherwise stable in flat Euclidean spaces. 4.2 Implementation Details Recognizing that hyperbolic geometry generalizes Euclidean geometry as the curvature approaches zero All models were trained for 100 epochs using the AdamW (limc→0 Dhyp (x, y) = 2∥x − y∥), we propose a Geometric Hybrid optimizer [33]. The learning rate was modulated by a half-cycle Strategy (GHS) to harmonize both spaces. While fusion can cosine annealing schedule, smoothly decaying to 10% of its technically be achieved via simple vector concatenation or initial value by the final epoch. We conducted a hyperparameter complex distance projection mechanisms, we empirically search for the initial learning rate across three values (1 × 10−4 , adopt a weighted-addition strategy (Fig. 3d) for its efficiency 1 × 10−5 , and 1 × 10−6 ), reporting the best performance for each and stability. For a given slide-level feature h, we generate a model. During training, we generally employed a batch size Euclidean embedding zE ∈ Em and a hyperbolic embedding of 8 and a maximum sequence length of 4,096 tile features. c zH ∈ Hm c , where zH = exp0 (WH h) utilizes the exponential For the PANDA dataset, these settings were adjusted to a batch map to incorporate the manifold curvature c. To bridge the size of 64 and a maximum of 512 tile features to accommodate geometric divide, we map the hyperbolic representation back dataset-specific distribution variances. Model performance was to the tangent space via the logarithmic map (logc0 ) and fuse it evaluated using five-fold cross-validation. We report the average with the Euclidean feature: C-index for survival prediction tasks and accuracy, AUROC, c zhybrid = α · zE + (1 − α) · log0 (zH ) (5) and F1-score for classification tasks. All experiments were implemented in Python 3.12.12 using PyTorch 2.9.0 and CUDA Here, α ∈ Rm is a learnable gating parameter (where m is the 12.8, running on four NVIDIA RTX 4090 GPUs. The founembedding dimension) that dynamically balances the two ge- dational preprocessing and training pipeline was supported by ometries. By maintaining the dimensionality and utilizing the UnPuzzle [34]. curvature c, this unified representation effectively combines the hierarchical embedding capacity of hyperbolic space with the 4.3 Comparison with SOTA Methods local morphological sensitivity of Euclidean space.
4
Experiments
4.1
Datasets and Downstream Tasks
We validate BatMIL on 6 core downstream tasks across seven WSI datasets, covering diverse diagnostic scenarios in computational pathology (Tab. 1). In the CAMELYON16 [29] and CAMELYON17 [30] datasets, the Breast Metastasis (BrMet) task evaluates the model’s capability to classify lymph nodes as normal or tumorous. The PANDA [31] dataset is used for ISUP grading (G-Score) to assess prostate cancer aggressiveness, which requires fine-grained analysis of cellular patterns. Furthermore, the TCGA [32] datasets are utilized to evaluate multiple diagnostic objectives. Specifically, the TCGANSCLC dataset is used to predict AJCC-defined tumor staging (T-Stage), a metric reflecting the tumor’s local progression. The TCGA-BRCA dataset evaluates HER2 (IHC-HER2) expression status to assess suitability for targeted therapy. For the Cancer Grading (GRADE) task, the TCGA-BLCA dataset is employed to determine tumor differentiation levels. Finally, the TCGA-CESC dataset, associated with the Lymphovascular Invasion (LymInv) task, identifies the subtle presence of cancer invasion into lymphatic or vascular channels.
We rigorously evaluate BatMIL against thirteen SOTA methods grouped into four mainstream architectures. Specifically, ABMIL [5], CLAM [10], DSMIL [11], and DTFDMIL [12] are attention-based methods; PatchGCN [13], ZoomMIL [14], and HiGT [15] are hierarchical methods; TransMIL [6] and GigaPath [16] are Transformer-based methods; and S4MIL [17], MambaMIL [18], MamMIL [19], and PathRWKV [20] are SSM-based methods. Performance is measured via 5-fold cross-validation using AUROC, Accuracy, and F1-Score. The quantitative results are presented in Tab. 2. Attention-based methods. Attention-based methods excel at isolating discriminative local patches but often struggle with global spatial context. On the CAMELYON17 dataset, BatMIL demonstrates superior generalization, exceeding DTFD-MIL by 6.2% in AUROC and outperforming CLAM by 2.0% in Accuracy. For tasks requiring fine-grained grading like PANDA, BatMIL’s AUROC, Accuracy, and F1-Score surpass CLAM by 1.3%, 2.4%, and 2.7%, respectively. This indicates that BatMIL effectively avoids the local feature bias commonly observed in standard attention mechanisms. However, for tasks highly dependent on specific localized biomarker expressions, such as IHC-HER2 (TCGA-BRCA) and LymInv (TCGA-CESC), BatMIL’s AUROC and F1-Score are slightly lower than optimal
Preprint – Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation
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Table 2: Performance comparison with thirteen methods. (Note: Standard deviations σ are represented by symbols: ‡ for σ ≤ 0.005, † for 0.005 < σ ≤ 0.015, and ∗ for σ > 0.015) Dataset
Metric (%)
ABMIL CLAM DSMIL DTFD-MIL PatchGCN ZoomMIL HiGT TransMIL GigaPath S4MIL MambaMIL MamMIL PathRWKV BatMIL [5]
AUROC
[12]
[13]
[14]
[15]
[6]
[16]
[17]
[18]
[19]
[20]
0.990†
0.967‡
0.973‡
0.965‡
0.993‡
0.987†
0.989∗
0.992‡
0.986‡
0.991‡
0.996‡
0.981
‡
0.947
‡
0.951
‡
0.945
†
0.982
‡
0.961
∗
0.984
‡
0.983
†
0.976
‡
0.983
†
0.988‡
0.980
‡
0.939
‡
0.944
‡
0.937
‡
0.981
‡
0.958
∗
0.983
†
0.982
†
0.975
‡
0.981
†
0.977∗
0.548
†
0.577
†
0.697
∗
0.541
0.546
†
0.722
∗
0.778∗
†
0.778∗
CAMELYON16 Accuracy 0.980 F1-Score
0.978
[10] ‡ ‡
0.708
∗
CAMELYON17 Accuracy 0.756
∗
AUROC
(Ours)
[11]
0.989‡ 0.990‡ 0.965‡ 0.981
‡
0.980
‡
0.710
∗
0.922
‡
0.915
†
0.710
∗
∗
0.716
†
0.587
∗
0.700
∗
∗
0.719
∗
0.758
∗
0.746
∗
0.545
∗
0.583
∗
0.570
∗
0.754
∗
0.731
∗
0.758
∗
0.667
∗
0.752
∗
0.498
†
0.503
∗
0.436
∗
0.463
∗
0.242
∗
0.309
∗
0.311
∗
0.404
∗
0.423
∗
0.244
∗
0.460
∗
0.303
∗
0.499
∗
0.507∗
0.946
‡
0.947
‡
†
0.944
‡
0.942
‡
0.944
†
0.940
‡
0.936
‡
0.940
‡
0.940
‡
0.942
‡
0.948
‡
0.947
‡
0.960‡
Accuracy 0.768
‡
0.774
‡
0.763
∗
0.762
†
0.744
†
0.749
‡
0.741
‡
0.739
†
0.752
†
0.759
‡
0.759
†
0.757
†
0.776
‡
0.798‡
F1-Score
0.711
†
0.721
†
0.701
∗
0.705
‡
0.705
‡
0.711
†
0.705
†
0.672
†
0.692
†
0.707
‡
0.693
†
0.699
‡
0.726
†
0.748†
0.983
†
0.986
‡
0.839
†
0.994
†
0.918
†
0.922
‡
0.913
‡
0.985
†
0.977
†
0.942
†
0.904
‡
0.938
†
0.991
‡
0.997‡
TCGA-BLCA Accuracy 0.932
‡
0.943
†
0.909
‡
0.954
†
0.966
‡
0.977
‡
0.955
‡
0.943
†
0.943
†
0.921
†
0.943
†
0.911
†
0.977
‡
0.977†
0.845
†
0.667
†
0.731
†
0.659
0.818
†
0.792
†
0.661
†
0.792
0.666
†
0.831
†
0.891† 0.549† 0.623†
F1-Score AUROC PANDA
AUROC
0.942
F1-Score
0.732
0.792
∗
0.576
AUROC
0.607
†
0.564
‡
0.619
‡
0.527
†
0.486
†
0.455
‡
0.455
‡
0.609
†
0.560
†
0.577
†
0.697
†
0.550
‡
0.492
‡
TCGA-BRCA Accuracy 0.607
†
0.574
†
0.519
†
0.612
†
0.585
†
0.519
†
0.514
†
0.536
†
0.443
†
0.574
‡
0.607
‡
0.579
‡
0.562
†
F1-Score
0.253† 0.254∗ 0.284†
0.255†
0.192†
0.219∗
0.192†
0.275†
0.226†
0.337†
0.286†
0.206†
0.304†
0.192∗
AUROC
0.621‡ 0.539† 0.604†
0.681†
0.566†
0.570†
0.570†
0.681†
0.506†
0.582†
0.588‡
0.677∗
0.708‡
0.602†
Accuracy 0.557‡ 0.519‡ 0.557‡
0.592∗
0.502‡
0.557‡
0.503‡
0.557‡
0.557‡
0.557‡
0.557‡
0.557‡
0.555†
0.593†
F1-Score
0.555‡ 0.516‡ 0.555†
0.584∗
0.555‡
0.540‡
0.540†
0.550†
0.550†
0.540‡
0.555†
0.555†
0.703‡
0.572‡
AUROC
0.590† 0.629† 0.610†
0.587†
0.595†
0.572†
0.559†
0.609†
0.599†
0.601†
0.594†
0.576†
0.596†
0.571†
TCGA-NSCLC Accuracy 0.468∗ 0.482† 0.509†
0.495†
0.532†
0.486†
0.518†
0.546†
0.491∗
0.500†
0.509†
0.468†
0.502‡
0.781†
0.305† 0.305∗ 0.313†
0.282†
0.277†
0.275∗
0.222†
0.314∗
0.041†
0.299†
0.286†
0.287†
0.270†
0.439†
F1-Score
∗
0.646
∗
TCGA-CESC
∗
0.756
∗
methods like DSMIL or DTFD-MIL, though it still maintains the highest Accuracy. This discrepancy suggests that while BatMIL is robust overall, classical attention models remain highly competitive when diagnostic evidence is extremely sparse and localized.
GigaPath across PANDA and TCGA-BLCA metrics. This demonstrates that BatMIL better balances global dependency tracking with local morphological preservation compared to standard self-attention architectures.
Hierarchical-based methods. Hierarchical methods are designed to explicitly model the multi-scale spatial topology of tissue. Compared to PatchGCN, ZoomMIL, and HiGT, BatMIL consistently establishes new state-of-the-art results across five datasets (CAMELYON16, CAMELYON17, PANDA, TCGABLCA, and TCGA-CESC). Most notably, in the challenging BrMet task on CAMELYON17, BatMIL improves upon ZoomMIL by remarkable margins of 19.1% in AUROC, 19.5% in Accuracy, and 19.8% in F1-Score. This demonstrates that BatMIL’s architectural design models multi-level tissue structures and tumor microenvironments far more effectively than existing graph- or tree-based hierarchical paradigms. While performance on TCGA-BRCA and TCGA-NSCLC shows minor fluctuations in F1-Score, BatMIL still secures the highest Accuracy and AUROC.
SSM-based methods. SSMs provide an efficient alternative for modeling extremely long sequences in WSIs. As shown in Tab. 2, BatMIL achieves the highest AUROC, Accuracy, and F1Score among all SSM-based competitors on CAMELYON17, PANDA, and TCGA-BLCA. Notably, BatMIL surpasses the strong PathRWKV baseline by 5.6% in AUROC on CAMELYON17 and 6.0% in F1-Score on TCGA-BLCA. Although S4MIL achieves a higher F1-Score on CAMELYON16 and TCGA-BRCA, BatMIL still dominates in overarching Accuracy. These results highlight that BatMIL more effectively adapts to heterogeneous pathological features and captures long-range dependencies without being heavily impacted by the sequencelength constraints or sample imbalances that challenge current SSM algorithms.
Transformer-based methods. Transformer-based models, such as TransMIL and GigaPath, are powerful in capturing global contextual representations. On the CAMELYON16 dataset, TransMIL achieves a marginally higher F1-Score than BatMIL. This minor drop can be attributed to the relatively small sample size of CAMELYON16, which may cause slight performance fluctuations in BatMIL. However, on larger or more heterogeneous datasets, BatMIL exhibits significant superiority. For instance, BatMIL outperforms TransMIL by 7.8% in AUROC on CAMELYON17 and significantly exceeds
4.4
Efficiency Analysis
Fig. 5 presents the performance and efficiency comparison on the PANDA dataset. The x-axis represents the inference time (s), the y-axis denotes the accuracy on the PANDA dataset, and the bubble size corresponds to the number of model parameters (Params(M)). BatMIL achieves the highest accuracy among all evaluated methods while maintaining a moderate parameter count of 9.76M and reasonable inference speed. Compared to heavier architectures such as GigaPath (86.34M) and ZoomMIL (86.30M), BatMIL delivers superior accuracy with significantly
Preprint – Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation
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Figure 4: Grad-CAM visualization for different MIL methods. Traditional Euclidean space-based methods exhibit highly scattered heatmaps, incorrectly attending to large swaths of benign background stroma. Some models manage partial localization but still suffer from prominent false-positive activations or dispersed background noise. Meanwhile, BatMIL accurately identifies the multi-scale spatial relationships among tiles. lower computational overhead. Conversely, when compared to 5 Discussion lightweight models like ABMIL (0.90M), MamMIL (0.99M), CLAM (1.19M), and S4MIL (1.35M), BatMIL yields substan- 5.1 Ablations on the Proposed Components tial accuracy improvements with only a marginal increase in inference time. Overall, BatMIL achieves an optimal tradeTo systematically evaluate the contribution of each aroff between diagnostic accuracy, computational efficiency, and chitectural innovation, we conducted ablation studies (Tab. 3) model complexity. alongside Grad-CAM visualization experiments (Fig. 6). Using S4MIL as the baseline, we first analyze the impact of the S4MoE backbone. Quantitatively, S4-MoE demonstrates superior capability in handling complex pathological tissues, improving 4.5 Visualization Analysis the AUROC by 0.053 on TCGA-BLCA and achieving an accuracy of 0.656 on TCGA-NSCLC. These improvements indicate To assess BatMIL’s ability to localize pathological regions that the MoE module effectively disentangles regional heteroduring WSI representation learning, we conduct Grad-CAM [35] geneity, preventing the parameter dilution often seen in unified visualization on the CAMELYON16 dataset. As shown in Fig. 4, networks when processing morphologically divergent tumor miwe present two representative cases, each including the raw WSI, croenvironments. This is directly mirrored in the qualitative ground-truth (GT) metastasis annotation, and attention heatmaps visualizations: while the baseline S4MIL often disperses attenfrom baseline methods and BatMIL. Red regions in the GT maps tion across large swaths of benign tissue, the dynamic routing in indicate metastatic lesions. S4-MoE effectively shifts the model’s focus toward pathological abnormalities, successfully filtering out background stroma. Traditional MIL methods encode patches in a single Euclidean feature space, limiting their ability to capture hierarchiBuilding upon the S4-MoE backbone, we explicitly ablate cal tissue topology and local morphological heterogeneity. As a the Geometric Hybrid Strategy (GHS) module by comparing result, their attention are often scattered and redundant. ABMIL the S4-MoE variant against the final BatMIL framework. This and CLAM produce diffuse activations that extend into normal comparison isolates the specific contribution of the dual geomettissue. DSMIL and PatchGCN partially highlight abnormal re- ric representation. We demonstrate that the choice of geometric gions but still introduce non-target responses. DSMIL falsely fusion strategy within the GHS module profoundly affects both activates on a benign region in the first case, while PatchGCN predictive performance and visual precision. We compared three shows weak background activations across normal breast tis- strategies: concatenation (BatMILC ), projection (BatMILP ), and sue, likely due to sensitivity to local intensity variations. These weighted-addition (BatMIL). As shown in Tab. 3, both BatMILC artifacts reduce their spatial alignment with true lesions. In and BatMILP fail to consistently outperform S4-MoE. Specificontrast, BatMIL generates more focused and precise heatmaps, cally, BatMILC degrades accuracy on PANDA due to the curse accurately highlighting metastatic regions while suppressing of dimensionality and feature redundancy, while BatMILP unactivations in adjacent normal tissue. Its attention distribution derperforms on the CAMELYON17 dataset because projecting closely matches the ground-truth annotations, demonstrating hyperbolic distances into a flat Euclidean space causes a critistronger localization ability and robustness to heterogeneous cal loss of its core exponential characteristics. Visually, these pathological backgrounds. fusion methods fail to elegantly integrate the complementary
Preprint – Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation
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Table 3: Ablations on the proposed S4-MoE and GHS modules. (Note: Standard deviations σ are represented by symbols: ‡ for σ ≤ 0.005, † for 0.005 < σ ≤ 0.015, and ∗ for σ > 0.015) Dataset
Metric (%) S4MIL S4-MoE BatMILC BatMILP BatMIL 0.989∗ 0.989∗
0.989†
0.989†
0.996‡
CAMELYON16 Accuracy 0.984‡ 0.984†
0.984†
0.978†
0.988‡ 0.977∗
AUROC
F1-Score 0.983
†
0.977
†
0.973
†
0.978
‡
AUROC
∗
0.450
†
0.547
†
0.457
†
0.778∗
CAMELYON17 Accuracy 0.646† 0.656∗
0.705†
0.636†
0.778∗
∗
0.507∗
0.541
0.198
†
0.505
0.426
†
0.949
†
0.950
†
0.941
†
0.960‡
Accuracy 0.759‡ 0.797‡
0.770†
0.774‡
0.798‡
0.715
‡
0.724
‡
0.748† 0.997‡
F1-Score 0.244 AUROC PANDA
0.724
†
F1-Score 0.707 AUROC
0.942
†
0.995
†
0.997
‡
0.996
†
TCGA-BLCA Accuracy 0.921
†
0.931
†
0.954
†
0.943
‡
0.977†
F1-Score 0.661† 0.731†
0.845†
0.792‡
0.891† 0.549†
0.577
†
0.634
†
0.601
†
0.655
‡
TCGA-BRCA Accuracy 0.574
‡
0.584
†
0.617
‡
0.562
†
0.623†
F1-Score 0.337† 0.224‡
0.204‡
0.287‡
0.192∗ 0.602†
0.582
†
0.596
†
0.596
†
0.593
†
Accuracy 0.557
‡
0.575
†
0.567
†
0.566
†
0.593†
F1-Score 0.540‡ 0.544†
0.556†
0.550†
0.572‡
0.587
†
0.501
†
0.571†
0.716
†
0.662
†
0.781†
0.425
†
∗
0.439†
AUROC
geometric priors. The two embedding spaces interfere with one another, resulting in highly redundant and scattered Grad-CAM activations that mistakenly attend to normal tissue regions. In contrast, BatMIL with the weighted-addition strategy consistently achieves the best overall performance across most core metrics. By utilizing a learnable gating parameter to balance the two spaces element-wise, this strategy prevents feature conflict. Visually, this translates to the most precise attention alignment with ground-truth annotations, concentrating heavily on metastatic and malignant regions with near-zero background noise. This confirms that the weighted-addition strategy creates a mathematically harmonious representation, successfully preserving both the global hierarchical embedding capacity of the hyperbolic space and the local morphological sensitivity of the Euclidean space to locate critical diagnostic features.
‡
‡
AUROC
Figure 5: Comparison with SOTA methods in inference time and parameters on the PANDA dataset. The scatter plot visualizes the trade-off between diagnostic accuracy (y-axis) and computational efficiency (x-axis), with bubble size representing the total number of model parameters (in millions). BatMIL (top-left) achieves the SOTA accuracy on the PANDA dataset while maintaining a moderate parameter count (9.76M) and competitive inference speed.
0.940
∗
TCGA-CESC
0.601
†
TCGA-NSCLC Accuracy 0.500
†
0.656
F1-Score 0.299
†
†
AUROC
0.603 0.440
† ∗
0.420
and TCGA-BRCA datasets for this curvature value. This behavior highlights a fundamental geometric trade-off. When hypc is too small (e.g., 0.05), the manifold becomes nearly flat, which strips the network of its ability to model the exponential growth of hierarchical tissue structures. Conversely, when hypc is too large (e.g., 0.2), the extreme negative curvature heavily distorts local geometric relationships, harming the model’s ability to discriminate fine-grained cellular patterns. The empirical optimum of hypc = 0.1 strikes the perfect mathematical balance, preserving the macroscopic hierarchical topology while maintaining microscopic morphological fidelity. 5.3
The Interpretability of MoE Routing Mechanisms
To examine whether the MoE router learns meaningful expert selection, we analyze both the standalone performance of each expert and the routing behavior on the CAMELYON16 5.2 Hyperbolic Distance Embedding Analysis dataset (Fig. 7). When evaluated independently, the four experts show different levels of discriminative ability, with Expert 3 The manifold curvature hypc is the defining parameter of achieving the best performance, followed by Expert 2, Expert 4, the Poincaré ball model, directly controlling its capacity to emand Expert 1. bed tree-like structures. To study its effect on BatMIL, we evaluated four curvature values: hypc ∈ {0.05, 0.075, 0.1, 0.2}. This performance hierarchy is reflected in the learned routAs demonstrated in Tab. 4, BatMIL achieves optimal perfor- ing probabilities. For each input token, our MoE module actimance across most datasets when hypc = 0.1. For example, vates only the top-2 experts among four candidates. The router on the CAMELYON16 dataset, the AUROC peaks at 0.996 for assigns the highest probabilities to Expert 3 and Expert 2, with hypc = 0.1, outperforming the configurations of 0.05, 0.075, and scores of 0.53 and 0.30, respectively. After top-2 selection, 0.2 by margins of 0.020, 0.011, and 0.025, respectively. Similar their routing weights are renormalized to 0.639 and 0.361, while performance peaks are observed in the PANDA, TCGA-BLCA, Expert 1 and Expert 4 are suppressed. This indicates that the
Preprint – Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation Raw Image
Ground Truth
S4MIL
S4-MoE
BatMIL C
9
BatMIL P
BatMIL
Figure 6: Grad-CAM visualization of ablation studies. S4MIL often disperses attention across benign tissues, while S4-MoE effectively shifts the focus toward pathological abnormalities. BatMILC and BatMILP exhibit scattered and redundant activations due to geometric feature interference. In contrast, BatMIL achieves the most precise attention alignment with the ground truth, successfully filtering out irrelevant background noise. Table 4: Performance comparison with different hypc . (Note: Standard deviations σ are represented by symbols: ‡ for σ ≤ 0.005, † for 0.005 < σ ≤ 0.015, and ∗ for σ > 0.015)
Router Weight 0
0.361
num_experts=4 0.639
0
top_k=2
Probability
Dataset
Metric (%) hypc = 0.05 hypc = 0.075 hypc = 0.1 hypc = 0.2 AUROC
0.976‡
0.985‡
0.996‡
0.971‡
CAMELYON16 Accuracy
0.967‡
0.977‡
0.988‡
0.963‡
F1-Score
‡
0.973‡
0.970
‡
0.977
∗
AUROC
0.754
∗
0.759
‡
0.778
∗
0.754†
CAMELYON17 Accuracy
0.755†
0.768∗
0.778∗
0.770∗
F1-Score
0.449
∗
0.503
†
0.507
∗
0.503†
AUROC
0.959
‡
0.954
‡
0.960
‡
0.955‡
Accuracy
0.786
‡
0.789
‡
0.798
‡
0.791‡
F1-Score
0.737†
0.735‡
0.748†
0.740‡
AUROC
0.976
‡
0.983
‡
0.997
‡
0.979‡
TCGA-BLCA Accuracy
0.977†
0.977†
0.977†
0.966†
F1-Score
0.877
‡
0.890
†
0.891
†
0.871†
AUROC
0.536†
0.545†
0.549†
0.545†
TCGA-BRCA Accuracy
0.615
†
0.619
†
†
0.610†
F1-Score
0.208
†
0.191
†
0.192
∗
0.199†
PANDA
0.967
0.623
AUROC
0.598†
0.600‡
0.602†
0.596†
Accuracy
0.587†
0.592†
0.593†
0.593‡
F1-Score
0.565
‡
0.566
‡
0.572
‡
0.564‡
AUROC
0.565
†
0.560
†
0.571
†
0.569†
TCGA-NSCLC Accuracy
0.773†
0.770†
0.781†
0.778†
F1-Score
0.427†
0.427†
0.439†
0.434†
TCGA-CESC
0.04
More importantly, the top-2 fusion strategy outperforms any individual expert. The fused MoE output achieves an AUROC of 0.989, an accuracy of 0.984, and an F1-score of 0.977, surpassing the best single expert, Expert 3. This suggests that Expert 2 provides complementary information that further improves the representation, whereas weaker experts may introduce noisy or less relevant features. Therefore, the sparse top2 routing mechanism provides an interpretable and effective balance between single-expert selection and dense expert aggregation, enabling BatMIL to selectively combine the most
0.53
0.13
Figure 7: Interpretability of the MoE routing mechanism on the CAMELYON16 dataset. The bar chart compares the individual performance of four experts against their fused performance. The router (num-experts=4, top-k=2) adaptively assigns the highest probabilities (0.53 and 0.30) to the two best-performing experts (Expert 3 and Expert 2). After weight renormalization (0.639 and 0.361, respectively), the sparse fusion of these two experts achieves the highest overall performance, demonstrating the advantage of selective expert activation over single-expert or dense routing strategies.
useful pathological representations while avoiding unnecessary computational cost. 5.4
router does not select experts arbitrarily; instead, it prioritizes the experts with stronger standalone representational capacity.
0.30
Limitations and Future Work
Despite establishing a robust geometry-aware paradigm, BatMIL exhibits certain limitations. In tasks where diagnostic evidence is extremely sparse and highly localized, such as predicting IHC-HER2 status in the TCGA-BRCA dataset, or identifying subtle Lymphovascular Invasion in TCGA-CESC dataset, BatMIL’s performance is slightly lower than classical attention models like DSMIL. This discrepancy implies that while BatMIL excels at modeling macro-architectural hierarchy and global context, its robust global prior may occasionally oversmooth extremely weak, highly localized cellular signals. Additionally, the current S4-MoE architecture employs a statically defined number of expert sub-networks (k=4). Given the varying
Preprint – Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation complexity across different cancer types, dynamically scaling the number of experts or introducing continuous routing mechanisms could further optimize resource allocation. Finally, while the S4 backbone maintains linear sequence complexity, maintaining dual geometric embeddings and sparse routing slightly increases memory overhead compared to ultra-lightweight aggregators. Addressing these sparse-signal sensitivities and optimizing the memory footprint of the hybrid geometric layer will be the primary focus of our future research toward clinical deployment.
6
Conclusion
10
[5] Maximilian Ilse, Jakub Tomczak, and Max Welling. Attention-based deep multiple instance learning. In International conference on machine learning, pages 2127– 2136. PMLR, 2018. [6] Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, et al. Transmil: Transformer based correlated multiple instance learning for whole slide image classification. Advances in neural information processing systems, 34:2136–2147, 2021. [7] Peixiang Huang, Yanyan Huang, Weiqin Zhao, Junjun He, and Lequan Yu. Hyperpath: Knowledge-guided hyperbolic semantic hierarchy modeling for wsi analysis. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 262–272. Springer, 2025.
In this paper, we proposed BatMIL, a novel framework designed to address the limitations of existing MIL methods that embed pathological tissue representations in a single Euclidean space while overlooking hierarchical structure and regional [8] Albert Gu. Modeling sequences with structured state spaces. Stanford University, 2023. heterogeneity. Specifically, we designed a hybrid hyperbolicEuclidean geometric representation that jointly embeds WSI [9] Hongrun Zhang, Yanda Meng, Yitian Zhao, Yihong Qiao, features into dual geometric spaces to facilitate hierarchical emXiaoyun Yang, Sarah E Coupland, and Yalin Zheng. Dtfdbedding. Building on this, we designed an S4-MoE module mil: Double-tier feature distillation multiple instance learnthat efficiently encodes long-range dependencies with linear ing for histopathology whole slide image classification. In computational complexity, and dynamically adapts to the proProceedings of the IEEE/CVF conference on computer nounced regional heterogeneity of pathological tissues through vision and pattern recognition, pages 18802–18812, 2022. a MoE routing mechanism. Extensive experiments on seven WSI datasets across six cancer types demonstrate that BatMIL [10] Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood. consistently outperforms thirteen SOTA MIL methods across Data-efficient and weakly supervised computational patholsix downstream tasks. Comprehensive ablation studies and inogy on whole-slide images. Nature biomedical engineerterpretability analyses further confirm the effectiveness of each ing, 5(6):555–570, 2021. component and the rationality of the weighted-addition strategy. In summary, BatMIL establishes a new paradigm for geometry- [11] Bin Li, Yin Li, and Kevin W Eliceiri. Dual-stream multiple aware representation learning in computational pathology, and instance learning network for whole slide image classificacould provide a promising direction for the future development tion with self-supervised contrastive learning. In Proceedof more accurate and interpretable clinical decision-support sysings of the IEEE/CVF conference on computer vision and tems. pattern recognition, pages 14318–14328, 2021. [12] Hongrun Zhang, Yanda Meng, Yitian Zhao, Yihong Qiao, Xiaoyun Yang, Sarah E Coupland, and Yalin Zheng. Dtfdmil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In [1] Matthew G Hanna, Anil Parwani, and Sahussapont Joseph Proceedings of the IEEE/CVF conference on computer Sirintrapun. Whole slide imaging: technology and applivision and pattern recognition, pages 18802–18812, 2022. cations. Advances in Anatomic Pathology, 27(4):251–259, 2020. [13] Richard J Chen, Ming Y Lu, Muhammad Shaban, Chengkuan Chen, Tiffany Y Chen, Drew FK Williamson, [2] Jiangbo Shi, Chen Li, Tieliang Gong, Chunbao Wang, and and Faisal Mahmood. Whole slide images are 2d point Huazhu Fu. Cod-mil: chain-of-diagnosis prompting multiclouds: Context-aware survival prediction using patchple instance learning for whole slide image classification. based graph convolutional networks. In International IEEE Transactions on Medical Imaging, 44(3):1218–1229, Conference on Medical Image Computing and Computer2024. Assisted Intervention, pages 339–349. Springer, 2021. [3] Richard J Chen, Tong Ding, Ming Y Lu, Drew FK Williamson, Guillaume Jaume, Bowen Chen, Andrew [14] Kevin Thandiackal, Boqi Chen, Pushpak Pati, Guillaume Jaume, Drew FK Williamson, Maria Gabrani, and Orcun Zhang, Daniel Shao, Andrew H Song, Muhammad Shaban, Goksel. Differentiable zooming for multiple instance learnet al. Towards a general-purpose foundation model for ing on whole-slide images. In European Conference on computational pathology. Nature Medicine, 2024. Computer Vision, pages 699–715. Springer, 2022. [4] Eric Zimmermann, Eugene Vorontsov, Julian Viret, Adam Casson, Michal Zelechowski, George Shaikovski, [15] Ziyu Guo, Weiqin Zhao, Shujun Wang, and Lequan Yu. Neil Tenenholtz, James Hall, David Klimstra, Razik Higt: Hierarchical interaction graph-transformer for whole Yousfi, et al. Virchow2: Scaling self-supervised mixed slide image analysis. In International Conference on Medimagnification models in pathology. arXiv preprint cal Image Computing and Computer-Assisted Intervention, arXiv:2408.00738, 2024. pages 755–764. Springer, 2023.
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