A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans.

ABSTRACT:

Background

Liver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals.

Methods

We designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM agg

SUBMITTER: Huang S 

PROVIDER: S-EPMC11450020 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature