arXiv AI
CRS-Bench: A Reference-Relative Reliability Benchmark for Medical Image Encoders
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arXiv Machine Learning
Jul 30
Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance
arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.
arXiv Computer Vision
4d ago
MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models
MVC-Bench is a new benchmark designed to evaluate the calibration of vision‑language models (VLMs) and medical VLMs (Medical‑VLMs) for medical image classification. It tests calibration across robustness to modality, backbone, and domain shift; effectiveness of calibration strategies and prompt‑tuning methods; and stability under prompt‑template and random‑seed variations. The benchmark includes eight backbones, three medical modalities (fundus imaging, histopathology, chest X‑ray), and compares post‑hoc, train‑time, and zero‑shot calibration approaches, reporting accuracy, Expected Calibration Error (ECE), Maximum Calibration Error (MCE), and Adaptive Calibration Error (ACE) over 1,638 experiments, while also proposing a Multi‑Class Margin (MCM) regularization technique that improves ECE in most settings.
arXiv AI
Jul 23
Self-supervision drives representational convergence in medical foundation models more than clinical supervision
arXiv:2607. 20274v1 Announce Type: cross Abstract: Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure.
arXiv Computer Vision
5d ago
Can You Trust Frozen Hematology Foundation Models under Acquisition Shift?
The study evaluates 15 frozen hematology foundation-model embeddings across four single‑cell acquisition domains, finding that while in‑domain accuracy is near‑saturated (macro‑F1 0.98–0.997), cross‑dataset performance drops dramatically (34–72%) and model rankings shift. Probe‑dependent rank transfer is observed, with 1‑NN retrieval more stable than linear heads, yet neither reliably predicts target robustness. Calibration deteriorates off‑domain (ECE rises from 0.004 to 0.35), and exposure to internal cohorts confounds shift analysis; a training‑free pseudo‑label‑balanced feature normalization (CBR) modestly improves target‑prior robustness and calibration. whyItMatters":"The findings highlight that frozen hematology foundation models, though accurate in‑domain, may fail under realistic scanner, site, and class‑prior shifts, underscoring the need for comprehensive audits of accuracy, calibration, exposure, and robustness before clinical deployment."
arXiv Computer Vision
5d ago
Label-Free Foundational Model Selection for Medical Image Classification under Distribution Shift via Pseudo Label Discrepancy
The paper introduces a label‑free method called AURCC for selecting the best foundational model for medical image classification when the target domain lacks labels. AURCC uses a pseudo‑label discrepancy computed by the SUDO framework to score models without fine‑tuning. Experiments on chest X‑ray data across three inter‑hospital shifts show that AURCC closely matches the true model ranking, outperforming simple source‑accuracy baselines especially when source data are limited.
arXiv Computation and Language
Aug 21
Calibrated Triage, Not Autonomy: Confidence Estimation for Medical Vision-Language Models
arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.