Medical Image Segmentation Benchmark

Medical image segmentation benchmarks evaluate the performance of algorithms that automatically delineate anatomical structures or lesions within medical images. Current research focuses on improving segmentation accuracy and robustness, particularly in semi-supervised settings with limited labeled data, and addressing domain shift issues across different medical centers. This involves exploring novel architectures like U-Net variations incorporating transformers or Kolmogorov-Arnold networks, and developing advanced training strategies such as contrastive learning, test-time adaptation, and diverse pseudo-labeling techniques. These advancements are crucial for improving the accuracy and reliability of computer-aided diagnosis and treatment planning in various medical applications.