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

Survival analysis is crucial to the evaluation of cancer treatment options and deep learning-
based methods integrating pathological images and genomic data have been used for …

Survival outcome assessment is challenging and inherently associated with multiple clinical
factors (eg, imaging and genomics biomarkers) in cancer. Enabling multimodal analytics …

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 …

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 …

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

The integration of multi-modal data, such as histopathological images and genomic data, is
essential for understanding cancer heterogeneity and complexity for personalized …

With the rapid advances in high-throughput sequencing technologies, the focus of survival
analysis has shifted from examining clinical indicators to incorporating genomic profiles with …

The rapidly emerging field of deep learning-based computational pathology has shown
promising results in utilizing whole slide images (WSIs) to objectively prognosticate cancer …

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 …