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arXiv Machine Learning

L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare Data

L2GSSL is a self‑supervised learning framework designed for volumetric healthcare data, particularly CT scans. It decomposes pretraining into intra‑slice and inter‑slice representation spaces, using a slice encoder for local, view‑invariant features and a global encoder to aggregate tokens for volumetric context. On the LIDC‑IDRI lung nodule malignancy classification task, L2GSSL achieves high AUROC scores with only 10–25% of labels, outperforming other self‑supervised baselines and approaching the performance of fully supervised models.

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arXiv Computer Vision
Aug 28

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

DALE-CT introduces depth‑aware 2D slice encoders that learn an anatomical world model of chest CT scans without 3D or positional supervision. By sampling self‑supervised views across a physical $z$‑axis slab, the encoder captures how anatomy changes between neighboring slices, enabling it to recover slice ordering and distinguish slices by anatomy alone. The model, trained on a large 287k‑scan corpus, achieves state‑of‑the‑art performance on CT‑RATE and is released with full code and evaluation tools.

By Evan W. Damron, Mahmut S. Gokmen, Mitchell A. Klusty, Caroline N. Leach, Emily B. Collier, V. K. Cody Bumgardner
llms
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Hugging Face Trending Papers
5d ago

BRACHIO: Broad Representations Across Centres for Head-and-neck Imaging Oncology (HECKTOR 2026)

The paper reports the submissions of team nucli to the HECKTOR 2026 challenge, which evaluates machine learning methods for head‑and‑neck cancer imaging. The team used self‑supervised pre‑trained encoders, training a 2D DINOv2 encoder on PET scans and combining a DynUNet with a SwinUNETR for segmentation, achieving a mean Dice of 0.5409. For TN staging, they fused geometric features from segmentation masks with PET‑DINOv2‑MIPs deep features via logistic regression, obtaining balanced accuracies of 0.3962 (T‑stage) and 0.6432 (N‑stage). For recurrence‑free survival prognosis, they fine‑tuned the PET‑DINOv2‑MIPs ViT‑S encoder with an MLP head under a CoxPH loss, achieving a Harrell's C‑index of 0.6577 and a weighted challenge score of 0.5802.

computer-visionfine-tuningbenchmarks
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arXiv Machine Learning
Oct 12

The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging

The paper introduces TAP-JEPA, a joint‑embedding predictive architecture trained on over 113,000 CT volumes, which achieves performance comparable to leading encoders in scan‑level classification. It demonstrates a predictability bias: while anatomy is largely predictable from context, pathology is not, leading to significant degradation in pathology detection when predictions are applied. The study audits the model’s predictive pathway, showing that pathology representations shift toward control regions and that removing pathology tokens minimally affects downstream decisions, underscoring that contextual predictability alone is insufficient for clinical relevance.

By Fabian Drexel, Bernhard Kainz, Jakob N. Kather, Daniel Rueckert, Lisa C. Adams, Keno K. Bressem, Cosmin I. Bercea
ragbenchmarkssafety
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arXiv Computer Vision
Sep 2

Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applications

Pix2Rep-v2 is a self‑supervised learning framework that learns pixel‑ and voxel‑level representations for dense medical imaging tasks, using a redundancy‑reduction objective and equivariance principles to scale to 3D and wide field‑of‑view data. The method is evaluated on four datasets across multiple modalities, tasks, and backbones, demonstrating higher data‑efficiency in few‑shot scenarios and competitive performance, such as a +9.3 Dice point improvement in one‑shot segmentation on the M&Ms‑2 dataset. An in‑context dense prototype approach is also proposed, eliminating the need for downstream training.

By S. Sifaoui, E. Angelini, S. Toupin, T. Pezel, L. Le Folgoc
computer-visionfine-tuningbenchmarks
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