Pathology-and-genomics multimodal transformer for survival outcome prediction
Survival outcome assessment is challenging and inherently associated with multiple clinical
factors (eg, imaging and genomics biomarkers) in cancer. Enabling multimodal analytics …
factors (eg, imaging and genomics biomarkers) in cancer. Enabling multimodal analytics …
TransSurv: transformer-based survival analysis model integrating histopathological images and genomic data for colorectal cancer
Survival analysis is a significant study in cancer prognosis, and the multi-modal data,
including histopathological images, genomic data, and clinical information, provides …
including histopathological images, genomic data, and clinical information, provides …
Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication
Background Deep learning models can infer cancer patient prognosis from molecular and
anatomic pathology information. Recent studies that leveraged information from …
anatomic pathology information. Recent studies that leveraged information from …
Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis
Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on
morphological information from histology slides and molecular profiles from genomic data …
morphological information from histology slides and molecular profiles from genomic data …
Cross-modal translation and alignment for survival analysis
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 …
analysis has shifted from examining clinical indicators to incorporating genomic profiles with …
Survival prediction via hierarchical multimodal co-attention transformer: A computational histology-radiology solution
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 …
demonstrated the promise of using whole slide images (WSIs) and radiology images for …
A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction
Morphological attributes from histopathological images and molecular profiles from genomic
data are important information to drive diagnosis, prognosis, and therapy of cancers. By …
data are important information to drive diagnosis, prognosis, and therapy of cancers. By …
Prototypical information bottlenecking and disentangling for multimodal cancer survival prediction
Multimodal learning significantly benefits cancer survival prediction, especially the
integration of pathological images and genomic data. Despite advantages of multimodal …
integration of pathological images and genomic data. Despite advantages of multimodal …
Deep learning with multimodal representation for pancancer prognosis prediction
A Cheerla, O Gevaert - Bioinformatics, 2019 - academic.oup.com
Motivation Estimating the future course of patients with cancer lesions is invaluable to
physicians; however, current clinical methods fail to effectively use the vast amount of …
physicians; however, current clinical methods fail to effectively use the vast amount of …
Hierarchical transformer for survival prediction using multimodality whole slide images and genomics
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 …
downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to …