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

Learning good representation of giga-pixel level whole slide pathology images (WSI) for
downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to …

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 …

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 …

The classification of gigapixel histopathology images with deep multiple instance learning
models has become a critical task in digital pathology and precision medicine. In this work …

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

Whole slide image (WSI) classification is a critical task in computational pathology, requiring
the processing of gigapixel-sized images, which is challenging for current deep-learning …

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

Multiple instance learning (MIL) has been increasingly used in the classification of
histopathology whole slide images (WSIs). However, MIL approaches for this specific …

Traditional image-based survival prediction models rely on discriminative patch labeling
which make those methods not scalable to extend to large datasets. Recent studies have …