Mome: Mixture of multimodal experts for cancer survival prediction
Survival prediction requires integrating Whole Slide Images (WSIs) and genomics, a task
complicated by significant heterogeneity and complex inter-and intra-modal interactions …
complicated by significant heterogeneity and complex inter-and intra-modal interactions …
M2EF-NNs: Multimodal Multi-instance Evidence Fusion Neural Networks for Cancer Survival Prediction
Accurate cancer survival prediction is crucial for assisting clinical doctors in formulating
treatment plans. Multimodal data, including histopathological images and genomic data …
treatment plans. Multimodal data, including histopathological images and genomic data …
Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction
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 …
risk of death, which generally benefits from the integration of histology and genomic data …
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 …
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival
L Pan, Y Peng, Y Li, Y Liang, L Xu, Q Liang… - arXiv preprint arXiv …, 2024 - arxiv.org
Integrating the different data modalities of cancer patients can significantly improve the
predictive performance of patient survival. However, most existing methods ignore the …
predictive performance of patient survival. However, most existing methods ignore the …
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 …
PG-MLIF: Multimodal Low-Rank Interaction Fusion Framework Integrating Pathological Images and Genomic Data for Cancer Prognosis Prediction
Precise prognostication can assist physicians in developing personalized treatment and
follow-up plans, which help enhance the overall survival rates. Recently, enormous amount …
follow-up plans, which help enhance the overall survival rates. Recently, enormous amount …
Multimodal co-attention transformer for survival prediction in gigapixel whole slide images
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 that involves modeling complex interactions within the tumor …
HC-MAE: Hierarchical Cross-attention Masked Autoencoder Integrating Histopathological Images and Multi-omics for Cancer Survival Prediction
Accurate cancer survival prediction enables clinicians to tailor treatment regimens based on
individual patient prognoses, effectively mitigating over-treatment and inefficient medical …
individual patient prognoses, effectively mitigating over-treatment and inefficient medical …
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 …