公开(公告)号：US20220262105A1

公开(公告)日：2022-08-18

申请号：US17625313

申请日：2020-07-17

Applicant: Zongwei ZHOU ,  Vatsal SODHA ,  Md, Mahfuzur RAHMAN SIDDIQUEE ,  Ruibin FENG ,  Nima TAJBAKHSH ,  Jianming LIANG ,  Arizona Board of Regents on behalf of Arizona State University

Inventor： Zongwei Zhou ,  Vatsal Sodha ,  Md Mahfuzur Rahman Siddiquee ,  Ruibin Feng ,  Nima Tajbakhsh ,  Jianming Liang

IPC: G06V10/774 ,  G06V10/82 ,  G06V10/98 ,  G06V10/776

Abstract: Described herein are means for generating source models for transfer learning to application specific models used in the processing of medical imaging. In some embodiments, the method comprises: identifying a group of training samples, wherein each training sample in the group of training samples includes an image; for each training sample in the group of training samples: identifying an original patch of the image corresponding to the training sample; identifying one or more transformations to be applied to the original patch; generating a transformed patch by applying the one or more transformations to the identified patch; and training an encoder-decoder network using a group of transformed patches corresponding to the group of training samples, wherein the encoder-decoder network is trained to generate an approximation of the original patch from a corresponding transformed patch, and wherein the encoder-decoder network is trained to minimize a loss function that indicates a difference between the generated approximation of the original patch and the original patch. The source models significantly enhance the transfer learning performance for many medical imaging tasks including, but not limited to, disease/organ detection, classification, and segmentation. Other related embodiments are disclosed.