Enhancing Robustness of Deep Learning to Batch Effects from Multi-site Data for Segmentation of Clinically Significant Prostate Cancer on MRI.

Sumedh Sonawane¹, Pranav Sompalle², Dihitha Baddula¹, Minoti Kedare¹, Natasha Setiadi³, Abhishek Midya⁴, Sena Azamat⁴, Zelin Zhang⁴, Dattatraya Patil⁵, Allison Yang⁶, Elias Kikano⁷, Danielle Hill⁶, Thomas Massuti⁶, Martin G Sanda⁵, Anant Madabhushi^4,8, Clinton D Bahler⁹, Rakesh Shiradkar¹⁰

¹Department of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing and Engineering, Indiana University Indianapolis, Indianapolis, IN, USA.

²Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.

³School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA.

⁴Wallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, GA, USA.

⁵Department of Urology, Emory University School of Medicine, Atlanta, GA, USA.

⁶Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.

⁷Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.

⁸Research Career Scientist, Atlanta Veterans Administration Medical Center, Atlanta, USA.

⁹Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA.

¹⁰Department of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing and Engineering, Indiana University Indianapolis, Indianapolis, IN, USA. rshirad@iu.edu.

PubMed DOI

Abstract

Deep learning (DL) has shown promise in segmenting clinically significant prostate cancer (csPCa) on MRI. However, batch effects arising from multi-site variations impact their generalizability. This study investigates the impact of training data harmonization, diversity, and few-shot fine-tuning on DL in segmenting csPCa on multi-site MRI. 3 T prostate MRI from N = 1822 patients of four sites (pu…

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