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:2609.39429v1 Announce Type: cross
Abstract: Uncertainty Quantification (UQ) is a key requirement for trustworthy AI in high-stakes medical image analysis. In this work, we evaluate UQ in a mult...
By Gonzalo Esteban Mosquera Rojas, Sebastian R. van der Voort, Carolin M. Pirkl, Sandeep Kaushik, Marion Smits, Stefan Klein
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:2609.16597v1 Announce Type: cross
Abstract: Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapp...
By Yinong Wang (Joyce), Jianwen Chen (Joyce), Zhou Chen (Joyce), Shuwen Kuang (Joyce), Haoning Jiang (Joyce), Yanzhao Shi (Joyce), Huichun Yuan (Joyce), Yan-ran (Joyce), Wang, Bing Wang, Lei Wu, Bin Tang, Li Meng, Baihua Luo, Bin Zhou, Wei Ding, Weiming Zhong, Wei Hou, Yuanbing Chen, Zhiping Wan, Wei Wang, Zhenkun Xiao, Wenwu Wan, Allen He, Yuyin Zhou, Longbo Zhang, Feifei Wang, Zhixiong Liu, Michael Iv, Xuan Gong, Liangqiong Qu
Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapping imaging features across tumor types, inter-obse...
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