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.

Metabolic Syndrome (MetS) is a prevalent condition associated with an increased risk of cardiovascular diseases, characterized by high blood pressure, hyperglycemia, and dyslipidemia. These risk factors not only exacerbate cardiovascular conditions but also impair immune function. Timely detection and prevention of MetS are imperative to mitigate these health risks. Recent advancements indicate various diagnostic approaches, including electrochemical biomarker detection, muscle mass to visceral fat ratio assessments, and anthropometric indices such as the body roundness index. Moreover, multimodality imaging techniques have become essential tools in comprehensive evaluation of MetS. This study introduces a cost-effective infrared thermal imager designed for MetS ordinary monitoring. Through simulation experiments involving 20 participants, 400 images of samples are collected and analyzed. The results demonstrate significant differences in thermal images between negative and positive samples. This innovative method could potentially offer a cost-effective and non-invasive tool for MetS monitoring.

Cervical cancer poses a significant threat to the health of women worldwide. Cervical cytopathology screening is an effective method for diagnosing cervical cancer. However, manual screening is time-consuming and prone to errors. The advent of automatic Computer-Aided Diagnosis (CAD) systems based on deep learning addresses this problem, however training these models requires large amounts of labeled data, which may not always be available. This paper proposes a Few-Shot Learning (FSL) framework called FSL-DSC to perform cervical cell classification tasks on small dataset. FSL-DSC first proposes inner loop learning and outer loop learning for individual tasks and overall parameter updates respectively, then a depthwise separable module is designed to further enhance the performance of the model. Among three repeated experiments, the FSL-DSC framework achieves an average accuracy of 83.34%, which shows the effectiveness and potential of the proposed FSL-DSC in the field of cervical image classification and few-shot tasks.

In the medical field, on the one hand, data mining can effectively establish evaluation models to supplement gold standards; on the other hand, it can guide the direction of scientific research by establishing connections between knowledge. Radiology images and pathological images are considered to be the most suitable medical data for data mining due to their large amount of information. Endometrial carcinoma is a common malignant tumor in women, and TP53 mutation status is an important factor affecting the occurrence and development of tumors. In this study, we propose a neural network structure based on multi-modal medical data that can predict TP53 mutations in endometrial carcinoma, with an accuracy of 86.21% in test set. Then, we clustered TP53-related deep learning features, and we believe that there is heterogeneity in TP53-related deep learning features.

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.