تقرير
Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
| العنوان: | Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning |
|---|---|
| المؤلفون: | Gupta1, Vikash, Roth, Holger, Buch3, Varun, Rockenbach, Marcio A. B. C., White, Richard D, Yang, Dong, Laur, Olga, Ghoshhajra, Brian, Dayan, Ittai, Xu, Daguang, Flores, Mona G., Erdal, Barbaros Selnur |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, I.2.10 |
| الوصف: | 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. Comment: 8 pages, 5 figures, pre-print |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2009.12437 |
| رقم الأكسشن: | edsarx.2009.12437 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |