Pathology Model

Pathology models leverage artificial intelligence to analyze digital histopathology images, aiming to automate tasks like tissue classification and biomarker detection, ultimately assisting pathologists in diagnosis and treatment planning. Current research emphasizes developing generalizable models through techniques like knowledge distillation and large-scale data generation (including image-text pairs), often employing vision-language models, convolutional neural networks, and graph convolutional networks tailored to the unique spatial characteristics of whole slide images. Data-centric approaches, focusing on data augmentation and curation strategies, are also crucial for improving model performance and robustness. These advancements hold significant promise for improving diagnostic accuracy, accelerating research, and personalizing cancer care.