Spectral decoupling for training transferable neural networks in medical imaging

iScience

Carolin Stürenberg¹  Esa Pitkänen²  Antti Rannikko³  Joona Pohjonen³  Tuomas Mirtti³ 

[1] Corresponding author;Department of Urology, Helsinki University Hospital, Helsinki, Finland;Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland;

关键词: Medical tests;    Medical imaging;    Algorithms;    Artificial intelligence;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: Many neural networks for medical imaging generalize poorly to data unseen during training. Such behavior can be caused by overfitting easy-to-learn features while disregarding other potentially informative features. A recent implicit bias mitigation technique called spectral decoupling provably encourages neural networks to learn more features by regularizing the networks' unnormalized prediction scores with an L2 penalty. We show that spectral decoupling increases the networks′ robustness for data distribution shifts and prevents overfitting on easy-to-learn features in medical images. To validate our findings, we train networks with and without spectral decoupling to detect prostate cancer on tissue slides and COVID-19 in chest radiographs. Networks trained with spectral decoupling achieve up to 9.5 percent point higher performance on external datasets. Spectral decoupling alleviates generalization issues associated with neural networks and can be used to complement or replace computationally expensive explicit bias mitigation methods, such as stain normalization in histological images.

【 授权许可】

Unknown