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

Processing giga-pixel whole slide histopathology images (WSI) is a computationally
expensive task. Multiple instance learning (MIL) has become the conventional approach to …

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 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 …

Abstract Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis
analysis are promising directions in computational pathology. However, limited by …

Abstract Multiple Instance Learning (MIL) has been widely applied in pathology towards
solving critical problems such as automating cancer diagnosis and grading, predicting …

Predicting survival rates based on multi-gigapixel histopathology images is one of the most
challenging tasks in digital pathology. Due to the computational complexities, Multiple …

Learning informative representations is crucial for classification and prediction tasks on
histopathological images. Due to the huge image size, whole-slide histopathological image …

Convolutional neural networks can be trained to perform histology slide classification using
weak annotations with multiple instance learning (MIL). However, given the paucity of …

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 …