Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning

Vikash Gupta1; Holger Roth; Varun Buch3; Marcio A. B. C. Rockenbach; Richard D White; Dong Yang; Olga Laur; Brian Ghoshhajra; Ittai Dayan; Daguang Xu; Mona G. Flores; Barbaros Selnur Erdal

arXiv · 2009.12437

Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning

Vikash Gupta1·Holger Roth·Varun Buch3·Marcio A. B. C. Rockenbach·Richard D White·Dong Yang·Olga Laur·Brian Ghoshhajra·Ittai Dayan·Daguang Xu·Mona G. Flores·Barbaros Selnur Erdal

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Abstract

The training of deep learning models typically requires extensive data, which are not readily available as large well-curated medical-image datasets for development of artificial intelligence (AI) models applied in Radiology. Recognizing the potential for transfer learning (TL) to allow a fully trained model from one institution to be fine-tuned by another institution using a much small local dataset, this report describes the challenges, methodology, and benefits of TL within the context of developing an AI model for a basic use-case, segmentation of Left Ventricular Myocardium (LVM) on images from 4-dimensional coronary computed tomography angiography. Ultimately, our results from comparisons of LVM segmentation predicted by a model locally trained using random initialization, versus one training-enhanced by TL, showed that a use-case model initiated by TL can be developed with sparse labels with acceptable performance. This process reduces the time required to build a new model in the clinical environment at a different institution.

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Vikash Gupta1, Holger Roth, Varun Buch3, Marcio A. B. C. Rockenbach, Richard D White, Dong Yang, Olga Laur, Brian Ghoshhajra, Ittai Dayan, Daguang Xu, Mona G. Flores, Barbaros Selnur Erdal. 2020-09-25. Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning. https://arxiv.org/abs/2009.12437

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