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.
The study examines how differences in radiologists’ reporting styles—such as terminology, shorthand, formatting, and detail—affect the evaluation of AI-generated chest X‑ray reports. By quantifying the sensitivity of common metrics to these variations, the authors show that changes in reference reports can shift model rankings. They introduce a taxonomy of reporting variations and a rewriting method, ReRef, that preserves clinical meaning while altering style, and release a validated dataset of paired reference reports to aid future research.
Sampling multiple reports creates an inference problem: how should sampled evidence be converted into a final decision, and does a better structured decision yield a better report? We study these ques...
arXiv:2607. 14984v1 Announce Type: new Abstract: Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect.
The paper introduces CELLAUDIT, a method for auditing whether inputs claimed to influence predictive models actually do so. By testing if an input can enter the computation, whether predictions depend on it, and if that dependence improves observed responses, the authors evaluate agent-generated predictors on a morphology‑transcriptomics benchmark (BBBC047). Their findings show that many models claim compound contributions that are not supported by the data, and that falsification‑guided revisions can recover genuine input effects while improving performance.
Whole-slide multiple-instance learning (MIL) observes only the patches admitted by its selector. Deployment can alter this selector through compute limits, tissue masking, or regional workflows, even...
The paper introduces DomSet-MIL, a multiple instance learning framework designed to recover evidence of coexisting lesions in whole-slide images when only the most severe diagnosis is weakly supervised. It models the dominant-label mechanism through a censored likelihood that treats the reported diagnosis as the maximum severity without assuming other lesions are absent, and employs severity-ordered residual routing with top‑q pooling to preserve secondary findings. Experiments on natural and controlled datasets show that DomSet-MIL outperforms standard MIL approaches in dominant-label prediction while better recovering latent coexisting-lesion evidence.
arXiv:2608. 05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.
arXiv:2607. 20848v1 Announce Type: new Abstract: Medical LLMs are often evaluated by whether they select the correct diagnosis, but diagnostic accuracy alone does not show whether the model used the case evidence appropriately.
arXiv:2509. 19671v3 Announce Type: replace Abstract: Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare.
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:2512. 22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biological findings.