Deep learning generates synthetic cancer histology for explainability and education.

ABSTRACT: Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with mol

SUBMITTER: Dolezal JM 

PROVIDER: S-EPMC10227067 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature