arXiv:2602.20773v2 Announce Type: replace
Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...
By Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot
arXiv:2606. 10713v1 Announce Type: cross Abstract: The nnU-Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biomedical data.
By Ana Sofia Santos, Andr\'e Ferreira, Gijs Luijten, Naida Solak, Lisle Faray de Paiva, Behrus Hinrichs-Puladi, Jens Kleesiek, Jan Egger, Victor Alves
arXiv:2609.37648v1 Announce Type: new
Abstract: Preoperative liver-tumor assessment requires segmentation, physical-space measurement, visual evidence, and resection planning from the same three-dime...
By Binghong Qian, Xuanhe Liu, Yifan Xing, Wenjie Deng, Jian Wu, Haochao Ying
arXiv:2603.23295v2 Announce Type: replace
Abstract: Radiotherapy workflows for oncological patients increasingly rely on multi-modal medical imaging, commonly involving both Magnetic Resonance Imagin...
By Konstantinos Barmpounakis, Theodoros P. Vagenas, Maria Vakalopoulou, George K. Matsopoulos
arXiv:2609.21698v1 Announce Type: new
Abstract: Deep learning segmentation of renal tumors requires large annotated datasets, yet clinical deployments typically offer only a handful of tumor-positive...
By Ekaterina Sedykh, Salme Ussanov, Dmytro Fedorenko, Dmytro Fishman
arXiv:2505. 07687v4 Announce Type: replace-cross Abstract: Multi-modal medical image synthesis is pivotal for alleviating clinical data scarcity, yet existing methods fail to reconcile global anatomical consistency with high-fidelity local detail.
By Feng Yuan, Yifan Gao, Haoyue Li, Xin Gao