Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma.

ABSTRACT:

Background

An accurate pathological diagnosis of hepatocellular carcinoma (HCC), one of the malignant tumors with the highest mortality rate, is time-consuming and heavily reliant on the experience of a pathologist. In this report, we proposed a deep learning model that required minimal noise reduction or manual annotation by an experienced pathologist for HCC diagnosis and classification.

Methods

We collected a whole-slide image of hematoxylin and eosin-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020. We propose a noise-specific deep learning model. The model was trained initially with 137 cases cropped into multiple-scaled datasets. Patch screening and dynamic label smoothing

SUBMITTER: Feng S 

PROVIDER: S-EPMC8671137 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature