The study evaluates the reliability of deep‑ensemble uncertainty for brain tumour segmentation on the BraTS‑GoAT dataset. A 5‑fold cross‑validated nnU‑Net baseline and a 3‑seed deep ensemble were compared for calibration and error detection; the ensemble showed modest gains in calibration on in‑distribution data but the single model’s confidence remained flat while accuracy degraded under synthetic corruptions. Disagreement among ensemble members rose sharply with corruption severity, proving to be a more sensitive indicator of acquisition shift than single‑model confidence.
By Riya Deepak Shet, Chenxi Liang, Le Zhang
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.
By Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
arXiv:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.
By Xin Ci Wong, Duygu Sarikaya, Kieran Zucker, Marc De Kamps, Nishant Ravikumar
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
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
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
arXiv:2608. 14766v1 Announce Type: cross Abstract: Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irreducible data ambiguity.
By Simon Baur, Arne Schernich, Ekin B\"oke, Wojciech Samek, Jackie Ma
arXiv:2604. 15271v3 Announce Type: replace-cross Abstract: Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision support.
By Tianhao Fu, Austin Wang, Charles Chen, Roby Aldave-Garza, Yucheng Chen
arXiv:2604.12411v2 Announce Type: replace
Abstract: Segmentation models based on deep neural networks demonstrate strong generalization for medical image segmentation. However, they often exhibit ove...
By Qiuyu Tian, Haoliang Sun, Yunshan Wang, Yinghuan Shi, Yilong Yin
arXiv:2607. 16317v1 Announce Type: cross Abstract: Deep networks now subtype brain tumors on MRI about as well as specialist readers, yet accuracy is not what keeps them out of the clinic.
By Medhansh Sharma
arXiv:2607. 22727v1 Announce Type: cross Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obvious warning.
By Pranav Kaliaperumal, Manisha Kaliaperumal
arXiv:2205. 04599v2 Announce Type: replace-cross Abstract: Explainable Artificial Intelligence (XAI) is essential for trustworthy AI in healthcare, yet many existing methods rely on technical explanations that are difficult for clinicians and patients to interpret.
By Mohammad Eslami, Solale Tabarestani, Saber Kazeminasab, Ehsan Adeli, Glyn Elwyn, Tobias Elze, Mengyu Wang, Nazlee Zebardast, Lucia Sobrin, Nassir Navab, Daniel Shu Wei Ting, Malek Adjouadi