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CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training.

Milad MostaviYu-Chiao ChiuYidong ChenYufei Huang
Published in: BMC bioinformatics (2021)
This work demonstrated, for the first time, the feasibility of predicting unseen cancer types whose samples are limited. Thus, it could inspire new and ingenious applications of one-shot and few-shot learning solutions for improving cancer diagnosis, prognostic, and our understanding of cancer.
Keyphrases
  • papillary thyroid
  • squamous cell
  • small cell lung cancer
  • squamous cell carcinoma
  • lymph node metastasis