DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis

[2608.14719] DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14719 (cs) [Submitted on 12 Aug 2026] Title: DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis Authors: Xiaoxiao Li , Xitong Ling , Jiawen Li , Weiming Chen , Zhenyang Cai , Xidong Wang , Tian Guan , Benyou Wang , Yonghong He View a PDF of the paper titled DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis, by Xiaoxiao Li and 8 other authors View PDF HTML (experimental) Abstract: Multiple instance learning (MIL) is widely used for weakly supervised whole slide image (WSI) analysis. However, under long-tailed distributions, MIL-based WSI analysis faces a nested dual long-tail: an inter-slide class long tail and an intra-slide long tail of instance-level discriminative evidence. The two long tails are coupled: tail classes have few training slides, while their limited diagnostic evidence is concentrated in a few patches and obscured by abundant within-bag redundancy. This coupling biases models toward head classes and degrades rare-class recognition. To address this, we propose DeCo-MIL for long-tailed WSI analysis, which jointly alleviates the nested dual long-tail through frequency-debiased counterfactual reasoning. For the inner long tail, DeCo-MIL clusters patches into tissue-morphology anchors, replaces each anchor with its matched normal prototype to perform a counterfactual intervention, and estimates its counterfactual contribution to the ground-truth class using class-frequency-corrected predictions. These contributions guide redundancy masking to preserve scarce discriminative instances. For the outer long tail, DeCo-MIL constructs anchor-stratified pseudo-bags from redundancy-reduced bags and combines tail-aware oversampling with consistency regularization, increasing effective supervision for tail classes while preserving tissue-morphology composition. Extensive experiments on three long-tailed WSI benchmarks demonstrate that DeCo-MIL achieves state-of-the-art performance in both tail-class recognition and overall classification. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14719 [cs.CV] (or arXiv:2608.14719v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14719 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xitong Ling [ view email ] [v1] Wed, 12 Aug 2026 03:52:31 UTC (6,139 KB) Full-text links: Access Paper: View a PDF of the paper titled DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis, by Xiaoxiao Li and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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