The paper introduces an uncertainty‑mapping framework for periventricular Fazekas score prediction, moving beyond standard performance metrics. By linking uncertainty to positions in the learned feature space, it identifies class‑boundary regions where predictions are ambiguous and highlights potential label disagreements. The study shows that different loss functions affect the uncertainty profile, with some models achieving clearer class separation and more localized uncertainty.
By Susanne Schmid, Johanna Ospel, Richard Frayne, Roberto Souza
The study investigates how confidence intervals (CIs) behave in medical imaging AI by analyzing 24 segmentation and classification tasks with 19 models per task, various metrics, aggregation strategies, and CI methods. It finds that required sample sizes for reliable CIs vary widely, CI behavior depends on performance metrics, aggregation strategy, and problem type, and that different CI methods differ in reliability and precision. The authors provide a decision tree to guide researchers in selecting appropriate CI methods, aiming to support future consensus guidelines on reporting performance uncertainty.
By Pascaline Andr\'e (Sorbonne Universit\'e, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, H\^opital de la Piti\'e-Salp\^etri\`ere, Paris, France), Charles Heitz (Sorbonne Universit\'e, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, H\^opital de la Piti\'e-Salp\^etri\`ere, Paris, France), Evangelia Christodoulou (German Cancer Research Center), Annika Reinke (German Cancer Research Center), Carole H. Sudre (Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine and Hawkes InstituteCentre for Medical Image Computing, Department of Computer Science, University College London, UK), Michela Antonelli (School of Biomedical Engineering and Imaging Science, King's College London, UK), Patrick Godau (German Cancer Research Center), M. Jorge Cardoso (School of Biomedical Engineering and Imaging Science, King's College London, UK), Antoine Gilson (Sorbonne Universit\'e, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, H\^opital de la Piti\'e-Salp\^etri\`ere, Paris, France), Sophie Tezenas du Montcel (Sorbonne Universit\'e, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, H\^opital de la Piti\'e-Salp\^etri\`ere, Paris, France), Ga\"el Varoquaux (SODA project team, Inria Saclay-\^Ile-de-France, France), Lena Maier-Hein (German Cancer Research Center), Olivier Colliot (Sorbonne Universit\'e, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, H\^opital de la Piti\'e-Salp\^etri\`ere, Paris, France)
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.
By Panagiotis Fytas, Ian Selby, Clemens Karner, Judith Babar, Simon Baker, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, John Li Chen, Alex Sawer, Michael Roberts, Jonathan Weir-McCall, J. H. F. Rudd, Carola-Bibiane Sch\"onlieb, Anna Korhonen, Anna Breger
arXiv:2609.15180v1 Announce Type: new
Abstract: Vision-language models are increasingly explored for clinical prediction from electronic health records and medical images, where identifying unreliabl...
By Mingcheng Zhu, Jinning Liang, Tingting Zhu
The paper investigates how pre‑training strategy, dataset size, and domain affect uncertainty estimation in vision medical foundation models. It compares point‑prediction calibration with conformal (region) prediction across retinal, histopathological, and chest X‑ray models, finding that domain‑specific, self‑supervised pre‑training yields better calibration and more efficient conformal sets. The study shows that standard recalibration alone cannot fully reconcile uncertainty differences between models trained on different data sources.
By Haoxu Huang, Narges Razavian
The paper examines whether model uncertainty aligns with human disagreement on vision tasks. Using multi‑annotator datasets (FER+ and CIFAR‑10H), the authors find that pretrained models rarely reflect the ambiguity humans perceive, with weak correlations between model confidence and human disagreement. Predictive multiplicity offers only modest improvement, indicating that common uncertainty metrics fail to flag ambiguous cases.
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.
By Andrew Wang, Jiashuo Zhang, Michael Oberst
Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content. Such control is essential because clinical scenarios diverge: emergency triage prioritizes sensitivity to reduce missed findings, whereas confirmatory interpretation emphasizes specificity to limit unnecessary interventions.
arXiv:2608.22059v1 Announce Type: cross
Abstract: Pretrained image encoders are central to medical image classification, where expert annotation is costly and task-specific cohorts are often limited....
By Xingtao Lin, Hangqi Ren, Caiwan Sun, You Chen
arXiv:2607. 20582v1 Announce Type: cross Abstract: Machine learning models for medical image analysis typically lack a reliable measure of confidence, limiting their use in ambiguous or atypical cases.
By Frederik Hauke, Patrick Wienholt, Christiane Kuhl, Dyke Ferber, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn
The paper introduces AdaConRed, a label‑free post‑conformal decision rule that transforms ambiguous conformal prediction sets into single class assignments for medical image classification. It employs a five‑stage pipeline—vision‑language generative augmentation, a frozen DermFoundation encoder, a lightweight MLP classifier, an entropy‑modulated margin‑aware nonconformity score, and a reassignment step for transitional samples—to improve accuracy on OSCC and ISIC benchmarks. Results show notable gains in malignant class accuracy while maintaining overall performance.
By Saibal Ghosh, Samarup Bhattacharya, Sanjoy Kumar Saha, Umapada Pal, Tapabrata Chakraborti
arXiv:2608. 10505v1 Announce Type: new Abstract: Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content.
By Ying Jin, Noel C. F. Codella, John Corring, Mu Wei, Dinei Florencio, Eric Horvitz