Machine learning-driven cancer diagnostics with improved robustness and interpretability
Pengfei Li, Zhen Liu · 2026
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Description
Pengfei Li and Zhen Liu survey machine learning for cancer diagnosis in a 2026 perspective. Early detection saves lives, yet building good diagnostic tests still leans on slow trial and error. Reading the flood of scan and molecular data also strains human interpretation.
The authors compare the main learning algorithms on data size, imbalance, and clarity of reasoning. They then gather recent wins, from tuning assay platforms to joining data across scales. Learning models can search design spaces far faster than manual tinkering. Hard problems remain, including skewed data and models that hide their logic. A roadmap closes the piece for builders of next-round tools.
Catalog record
| CREATOR | Pengfei Li, Zhen Liu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 80 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |