Biased data, biased AI: deep networks predict the acquisition site of TCGA images.

ABSTRACT:

Background

Deep learning models applied to healthcare applications including digital pathology have been increasing their scope and importance in recent years. Many of these models have been trained on The Cancer Genome Atlas (TCGA) atlas of digital images, or use it as a validation source. One crucial factor that seems to have been widely ignored is the internal bias that originates from the institutions that contributed WSIs to the TCGA dataset, and its effects on models trained on this dataset.

Methods

8,579 paraffin-embedded, hematoxylin and eosin stained, digital slides were selected from the TCGA dataset. More than 140 medical institutions (acquisition sites) contributed to this dataset. Two deep neural networks (DenseNet121 and KimiaNet were used to extract deep featu

SUBMITTER: Dehkharghanian T 

PROVIDER: S-EPMC10189924 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature