Cross-modality Attention-based Multimodal Fusion for Non-small Cell Lung Cancer (NSCLC) Patient Survival Prediction

arXiv:2308.09831v2 Announce Type: replace-cross
Abstract: Cancer prognosis and survival outcome predictions are crucial for therapeutic response estimation and for stratifying patients into various treatment groups. Medical domains concerned with cancer prognosis are abundant with multiple modalities, including pathological image data and non-image data such as genomic information. To date, multimodal learning has shown potential to enhance clinical prediction model performance by extracting and aggregating information from different modalities of the same subject. This approach could outperform single modality learning, thus …

abstract arxiv attention cancer cs.cv data domains eess.iv fusion genomic image image data information lung cancer medical multimodal multiple patient patients prediction predictions small survival treatment type