Abstract Background and Objective High-resolution histopathology whole slide images
(WSIs) contain abundant valuable information for cancer prognosis. However, most …

The rapid advances in deep learning-based computational pathology and radiology have
demonstrated the promise of using whole slide images (WSIs) and radiology images for …

Histopathology image-based survival prediction aims to provide a precise assessment of
cancer prognosis and can inform personalized treatment decision-making in order to …

Computational pathology, utilizing whole slide image (WSI) for pathological diagnosis, has
advanced the development of intelligent healthcare. However, the scarcity of annotated data …

Survival prediction based on whole slide images (WSIs) is a challenging task for patient-
level multiple instance learning (MIL). Due to the vast amount of data for a patient (one or …

Survival analysis is a significant study in cancer prognosis, and the multi-modal data,
including histopathological images, genomic data, and clinical information, provides …

Recently, digital pathology image-based prognosis prediction has become a hot topic in
healthcare research to make early decisions on therapy and improve the treatment quality of …

Understanding of prognosis and mortality is crucial for evaluating the treatment plans for
patients. Recent developments of digital pathology and deep learning bring the possibility of …

Survival prediction is a complicated ordinal regression task that aims to predict the ranking
risk of death, which generally benefits from the integration of histology and genomic data …

Survival prediction using whole slide images (WSIs) can provide guidance for better
treatment of diseases and patient care. Previous methods usually extract and process only …