Pathological Feature

Pathological feature analysis focuses on automatically extracting meaningful information from various biological data sources, such as DNA sequences, medical images (e.g., whole slide images, CT scans), and clinical records, to improve disease diagnosis and prognosis. Current research emphasizes the development and application of advanced machine learning models, including deep learning architectures like convolutional neural networks, vision transformers, and language models, often incorporating multi-modal data integration and few-shot learning techniques to address data scarcity issues. These advancements hold significant promise for improving the accuracy and efficiency of disease detection, particularly in areas like cancer diagnosis and the identification of rare diseases, ultimately leading to better patient care and treatment strategies.