arXiv Computer Vision September 2, 2026 By S. Sifaoui, E. Angelini, S. Toupin, T. Pezel, L. Le Folgoc

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

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv AI

Jun 2

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Systematic Review and Practical Design Guidelines

arXiv:2605. 23995v2 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) has emerged as a promising paradigm for addressing the annotation bottleneck in medical imaging by learning representations from unlabeled data.

By Chathura Wimalasiri

computer-vision safety

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

arXiv AI

Jul 1

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines

arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.

By Chathura Wimalasiri, Kishor Nandakishor, Marimuthu Palaniswami

computer-vision

arXiv Machine Learning

Jul 8

Towards Interpretable Foundation Models for Retinal Fundus Images

arXiv:2603. 18846v3 Announce Type: replace-cross Abstract: Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL).

By Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi, Philipp Berens

safety

arXiv Computer Vision

Sep 7

DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models

DART is a new RGB‑D pretraining method for surgical vision foundation models that incorporates pseudo‑labeled depth maps as a pixel‑space reconstruction target during training. By adding a depth reconstruction head to DINOv2’s masked iBOT framework, DART improves representation quality without affecting downstream RGB‑only fine‑tuning or inference. Across eight surgical benchmarks—including segmentation, depth estimation, and image‑level recognition—DART outperforms both natural‑image and in‑domain baselines, demonstrating that geometric pseudo‑labels can strengthen foundation model pretraining without extra labels or inference cost.

By John J. Han, Adam Schmidt, Muhammad Abdullah Jamal, Jie Ying Wu, Omid Mohareri

computer-vision fine-tuning benchmarks

arXiv Machine Learning

Jun 3

CoralBay: A Self-Supervised CT Foundation Model

arXiv:2606. 03888v1 Announce Type: cross Abstract: Self-supervised learning has enabled large-scale pre-training on 2D natural images, producing general-purpose visual representations that transfer effectively across tasks.

By Ioannis Gatopoulos, Nicolas K\"anzig, Sebastian Ot\'alora, Fei Tang

efficiency benchmarks