DALE-CT: Depth-Aware 2D Slice Encoders Learn an Anatomical World Model of Chest CT

[2606.07775] DALE-CT: Depth-Aware 2D Slice Encoders Learn an Anatomical World Model of Chest CT Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2606.07775 (cs) [Submitted on 5 Jun 2026 ( v1 ), last revised 26 Aug 2026 (this version, v2)] Title: DALE-CT: Depth-Aware 2D Slice Encoders Learn an Anatomical World Model of Chest CT Authors: Evan W. Damron , Mahmut S. Gokmen , Mitchell A. Klusty , Caroline N. Leach , Emily B. Collier , V. K. Cody Bumgardner View PDF HTML (experimental) Abstract: Chest CT is among the highest-volume imaging exams in medicine, yet expert voxel-level annotations are scarce and costly, motivating encoders that learn directly from unlabeled scans. We present DALE-CT, a family of 2D slice-based Vision Transformers trained from scratch on chest CT with the heuristics-free LeJEPA objective. We introduce depth-aware slab sampling, which draws self-supervised views from across a physical $z$-axis slab rather than a single slice, implicitly tasking the 2D encoder with representing how anatomy changes between neighboring slices. The frozen representations trace each scan as a smooth anatomical trajectory, recover cranio-caudal slice ordering without labels, and distinguish slices by the anatomy they contain rather than by position alone. This anatomical world model emerges without any 3D or positional supervision, and an otherwise-identical encoder trained on slices in isolation never develops it. Building on this backbone, we introduce dense auxiliary supervision into the pretraining objective, using anatomical and abnormality masks to supervise patch and slice tokens alongside the self-supervised loss, and we compare the resulting variants against a DINOv2 baseline continually pretrained on CT-RATE. Among nine public and in-house models evaluated under the same protocol, DALE-CT-2S is the strongest 2D model in-domain, reaching 0.825 Macro AUROC on CT-RATE, within 0.024 of COLIPRI-CRM and without any text supervision. We subsequently scale the supervision-free configuration to a $\sim$287k-scan multi-source pool, to our knowledge the largest reported chest-CT pretraining corpus. The resulting DALE-CT-0-L posts the best 2D external-transfer point estimates, and we release it as our recommended backbone with the full model family, training code, and evaluation pipeline. Comments: 18 pages, 5 figures, 13 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2606.07775 [cs.CV] (or arXiv:2606.07775v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.07775 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Evan Damron [ view email ] [v1] Fri, 5 Jun 2026 18:39:05 UTC (2,941 KB) [v2] Wed, 26 Aug 2026 20:37:37 UTC (4,613 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2026-06 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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