Automatic Medical Generated Analysis Based on Histopathology Image Segmentation Half UNet

This study reviews and benchmarks SAM2, YOLOv8, UNet, and Half UNet for histopathology image segmentation, integrating outputs with biomedical language models like BioGPT, BioBERT, and DeepSeek VL to generate diagnostic reports. Experiments on the TCGA dataset show that Half UNet offers efficient, accurate segmentation, while SAM2 excels in few-shot settings. Combining segmentation with language models enhances interpretability and automation, improving workflow efficiency and diagnostic accuracy. However, challenges remain in generalizing across tissue types and staining methods. Overall, the integrated approach marks significant progress toward fully automated histopathology analysis