Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data. (arXiv:2203.04419v2 [cs.LG] UPDATED)

Integrating cross-department multi-modal data (e.g., radiological,
pathological, genomic, and clinical data) is ubiquitous in brain cancer
diagnosis and survival prediction. To date, such an integration is typically
conducted by human physicians (and panels of experts), which can be subjective
and semi-quantitative. Recent advances in multi-modal deep learning, however,
have opened a door to leverage such a process to a more objective and
quantitative manner. Unfortunately, the prior arts of using four modalities on
brain cancer survival prediction are limited by …

arxiv brain cancer data genomics lg prediction radiology survival