Survival prediction for patients based on histopa-thological whole-slide images (WSIs) has
attracted increasing attention in recent years. Due to the massive pixel data in a single WSI …

Patient survival prediction based on gigapixel whole-slide histopathological images (WSIs)
has become increasingly prevalent in recent years. A key challenge of this task is achieving …

Abstract Background and Objective Predicting patients' survival from gigapixel Whole-Slide
Images (WSIs) has always been a challenging task. To learn effective WSI representations …

The emergence of deep learning in the medical field has popularized the development of
models to predict survival outcomes from histopathology images in precision oncology …

Survival outcome prediction is a challenging weakly-supervised and ordinal regression task
in computational pathology that involves modeling complex interactions within the tumor …

In computational pathology, multi-gigapixel whole slide images (WSIs) are typically divided
into small patches because of their extremely large size and memory requirements …

Histopathological Whole Slide Images (WSIs) at giga-pixel resolution are the gold standard
for cancer analysis and prognosis. Due to the scarcity of pixel-or patch-level annotations of …

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

Deep neural networks have been used in survival prediction by providing high-quality
features. However, few works have noticed the significant role of topological features of …