arXiv Machine Learning June 29, 2026 By Francesco Di Salvo, Sebastian Doerrich, Christian Ledig

MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoring

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arXiv:2606. 22649v2 Announce Type: replace-cross Abstract: Foundation models provide highly descriptive representations for medical images, yet their reliability degrades under distribution shifts arising from changes in patients, devices, or acquisition conditions.

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arXiv Machine Learning

Jul 21

VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space

arXiv:2601. 18823v4 Announce Type: replace Abstract: Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data.

By Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado

diffusion benchmarks

arXiv AI

Jun 30

Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach

arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.

By Pritam Kar, Gouri Lakshmi S, Saptarshi Bej

rag diffusion benchmarks

arXiv AI

Jun 11

OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection

arXiv:2507. 21164v2 Announce Type: replace-cross Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not available.

By Nicolas Pinon (MYRIAD), Robin Trombetta (MYRIAD), Carole Lartizien (MYRIAD)

benchmarks

arXiv Computer Vision

Sep 22

MarsRecon: Self-Supervised and Multimodal Surface Representations for Mars

arXiv:2609.22379v1 Announce Type: new Abstract: High-resolution orbital imagery offers a rich record of the Martian surface, but sparse geological labels limit supervised representation learning. We...

By Akshay Naik, Marius F. R. Juston, Jay Mahajan

rag diffusion multimodal

Hugging Face Trending Papers

Sep 8

Layer Selection in VLMs for Zero-Shot OOD Detection via Multi-Resolution Entropy Estimation

The paper investigates zero‑shot out‑of‑distribution (OOD) detection in medical imaging using vision‑language models (VLMs). It shows that intermediate layers, rather than only the final layer, provide valuable OOD signals and that the best layer depends on the imaging modality. To overcome instability in entropy‑based layer selection, the authors introduce a multi‑resolution entropy estimation that aggregates histogram statistics across scales, achieving consistent improvements over state‑of‑the‑art methods on the MIDOG and OASIS benchmarks.

rag diffusion multimodal benchmarks

arXiv Computer Vision

Aug 24

When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuning

arXiv:2608.21300v1 Announce Type: new Abstract: Foundation models for medical image segmentation, like prompt-based MedSAM, generalize well across domains and modalities, often in zero or few-shot se...

By Marko Haralovi\'c, Sounic Akkaraju, Carlo Baretta, Vasil Zapryanov, Alexia Briassouli

computer-vision fine-tuning benchmarks safety