Medical Image Segmentation
Medical image segmentation aims to automatically delineate specific anatomical structures or regions of interest within medical images, facilitating accurate diagnosis and treatment planning. Current research heavily focuses on improving segmentation accuracy and efficiency using advanced architectures like U-Net and its variants, Vision Transformers, and Large Language Models, often incorporating techniques such as multi-scale feature extraction, attention mechanisms, and test-time training. These advancements are crucial for improving diagnostic capabilities, accelerating clinical workflows, and enabling more precise and personalized medicine. Furthermore, research is actively addressing challenges like limited annotated data through semi-supervised learning and the use of foundation models for improved generalization across different imaging modalities and clinical settings.
Papers
Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation
Heng Li, Haojin Li, Wei Zhao, Huazhu Fu, Xiuyun Su, Yan Hu, Jiang Liu
EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation
Yingyu Chen, Ziyuan Yang, Chenyu Shen, Zhiwen Wang, Yang Qin, Yi Zhang
Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging
Heejong Kim, Victor Ion Butoi, Adrian V. Dalca, Daniel J. A. Margolis, Mert R. Sabuncu
Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning
Hritam Basak, Zhaozheng Yin
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation
Fubao Zhu, Yanhui Tian, Chuang Han, Yanting Li, Jiaofen Nan, Ni Yao, Weihua Zhou
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification
Tao Wang, Xinlin Zhang, Yuanbo Zhou, Junlin Lan, Tao Tan, Min Du, Qinquan Gao, Tong Tong