arXiv:2607. 14338v1 Announce Type: cross Abstract: Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced segmentation tasks.
By Laurin Lux, Alexander H. Berger, Moritz Knolle, Daniel R\"uckert, Johannes C. Paetzold
Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced segmentation tasks. However, models trained using region-based loss functions are notoriously miscalibrated and typically yield over-confident predictions.
arXiv:2608.20942v1 Announce Type: new
Abstract: Motivated by the classical Chan-Vese model and the ability of deep priors to capture complex spatial structures, we develop a segmentation model that l...
By Shuangshuang Duan, Chunlei He, Shoujun Huang, Dexing Kong
arXiv:2608. 12196v1 Announce Type: cross Abstract: Purpose: Deep learning-based medical image segmentation has achieved remarkable success, yet purely data-driven approaches often fail to exploit the rich mathematical structure inherent in medical images.
By Jing Zhu, Ye Wang, Fumin Wang
arXiv:2608. 19965v1 Announce Type: cross Abstract: Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex topology, severe class imbalance, weak contrast, and large variations in structure morphology.
By Sidi Mohamed Sid'El Moctar, Nicolas Vitry, H\'el\`ene Bouvrais
arXiv:2606. 16153v1 Announce Type: cross Abstract: Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification.
By Pengyu Zhu, Xiaojing Zhang, Kunbo Zhang, Chunyan Zhang, Zhenyu Wang