This work proposes a Few-shot network employed for Perirenal Fat and Renal Parenchyma Semantic Segmentation (PRS-Net) in Diabetic Kidney Disease (DKD). PRS-Net integrates ST module and Fusion module. These two modules allow the model to process CT images with different spatial distributions and fuse multi-scale features, thereby enhancing performance in image segmentation. Utilizing the Perirenal Fat and Renal Parenchyma Dataset for semantic segmentation, PRS-Net achieves a mean intersection over union of 60.22% on test set, achieving superior performance compared to the other models. PRS-Net has clinical significance for early DKD diagnosis.

For esophageal cancer immunotherapy, Programmed Death Ligand-l (PD-LI) is considered a predictive biomarker. However, immunehistochemistry (IHC) methods used to quantify PD-LI are challenged by high cost, time and variability. In contrast, hematoxylin and eosin (H&E) staining is a reliable method commonly used in cancer diagnosis. By employing advanced deep learning techniques, this study demonstrates the feasibility of predicting PD-LI expression from H&E stained images. With the help of pathologists, a dataset is constructed to evaluate the validity of PD-LI prediction in esophageal cancer by H&E using the FusedNet model. In 227 patients, PD-LI status is systematically predicted. Consistent prediction performance is demonstrated through validation of the validation set, proving that the system can be used as a decision support and quality assurance system in clinical practice.