Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.

ABSTRACT:

Objectives

To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading.

Methods

Totally 466 patients undergoing partial hepatectomy were enrolled, including 401 with chronic hepatitis B and 65 without fibrosis pathologically. All patients received elastography and got liver stiffness measurement (LSM) 2-3 days before surgery. We proposed a deep convolutional neural network by TL to analyze images of gray scale modality (GM) and elastogram modality (EM). The TL process was used for liver fibrosis classification by Inception-V3 network which pretrained on ImageNet. The diagnostic performance of TL and non-TL was compared. The value of single modalities, includ

SUBMITTER: Xue LY 

PROVIDER: S-EPMC7160214 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature