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

• URL: http://arxiv.org/abs/2009.12437v1
• Date: Fri, 25 Sep 2020 21:12:50 GMT
• Title: Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
• Authors: Vikash Gupta1 and Holger Roth and Varun Buch3 and Marcio A.B.C. Rockenbach and Richard D White and Dong Yang and Olga Laur and Brian Ghoshhajra and Ittai Dayan and Daguang Xu and Mona G. Flores and Barbaros Selnur Erdal
• Abstract summary: Transfer learning (TL) allows 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.
• Score: 8.043077408518826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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