Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer
Yang Bo, Cai Hongyi, Li Hao · 2026
Read Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer by Yang Bo, Cai Hongyi, Li Hao (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13518630.txt.
Description
Yang Bo, Cai Hongyi, and Li Hao review progress in 2026 on deep learning models that combine several kinds of patient data to predict outcomes in stomach cancer. Single sources such as scans, tissue slides, genes, or clinical records each capture only part of a tumor. Fusion models join those layers to sort patients by risk in a more personal way.
The review covers core methods, current prognostic models, shared datasets, and ways of judging model quality. Yang and co-authors also weigh hard problems: uneven data, hard-to-explain model choices, and slow movement into clinics. Readers get a map of where combined-data prediction stands and what must improve before hospitals can rely on it.
Catalog record
| CREATOR | Yang Bo, Cai Hongyi, Li Hao |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33516 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |