arXiv:2606. 29586v1 Announce Type: cross Abstract: Vision-language foundation models have shown strong potential in medical image analysis.
By Hang Su, Chao Sun, Zhaofan Li, Wei Hu, Juhua Liu, Bo Du
arXiv:2607. 16705v1 Announce Type: cross Abstract: Semi-supervised learning (SSL) is an effective solution for medical image segmentation with limited annotations.
By Shao-feng Jiang, Zhe-yang Jing, Qin Lu, Huan-huan Shi, Zhen Chen, Cong-xuan zhang, Chen Yi
arXiv:2606. 16868v1 Announce Type: cross Abstract: While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels.
By Markus Bujotzek, Dimitrios Bounias, Stefan Denner, Ralf Floca, Maximilian Fischer, Peter Neher, Klaus Maier-Hein
Multimodal based approaches often outperform single modality approaches in downstream tasks as the different modalities provide complementary information, yet acquiring paired clinical data remains a significant challenge in real world scenarios. While cross-modal knowledge distillation addresses this, existing methods often struggle with large modality gaps and the propagation of noise from uncertain source-domain predictions.
arXiv:2510. 12953v4 Announce Type: replace-cross Abstract: Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection.
By Xiao He, Huangxuan Zhao, Guojia Wan, Jiancheng Pan, Yanxing Liu, Yong Luo, Juhua Liu, Yongchao Xu, Wei Zhou, Dacheng Tao, Bo Du
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