Pathological Image

Pathological image analysis focuses on automatically extracting meaningful information from digitized microscope slides, primarily for disease diagnosis and prognosis. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and transformer-based architectures like Swin Transformers, often incorporating techniques like transfer learning, self-supervised learning, and multiple instance learning to address challenges such as limited annotated data and stain variations. These advancements aim to improve the speed, accuracy, and objectivity of pathological diagnoses, ultimately impacting patient care and accelerating biomedical research.