Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction

arXiv:2403.11375v1 Announce Type: cross
Abstract: For predicting cancer survival outcomes, standard approaches in clinical research are often based on two main modalities: pathology images for observing cell morphology features, and genomic (e.g., bulk RNA-seq) for quantifying gene expressions. However, existing pathology-genomic multi-modal algorithms face significant challenges: (1) Valuable biological insights regarding genes and gene-gene interactions are frequently overlooked; (2) one modality often dominates the optimization process, causing inadequate training for the other modality. In this paper, we introduce a new …

abstract algorithms arxiv bulk cancer challenges clinical clinical research cs.cv cs.lg face features framework gene genomic however images insights modal multi-modal path pathology prediction q-bio.gn research rna rna-seq standard survival type