Weakly Supervised Deep Learning Can Analyze Focal Liver Lesions in Contrast-Enhanced Ultrasound.

ABSTRACT:

Introduction

Assessing the malignancy of focal liver lesions (FLLs) is an important yet challenging aspect of routine patient care. Contrast-enhanced ultrasound (CEUS) has proved to be a highly reliable tool but is very dependent on the examiner's expertise. The emergence of artificial intelligence has opened doors to algorithms that could potentially aid in the diagnostic process. In this study, we evaluate the performance of a weakly supervised deep learning model in classifying FLLs as malignant or benign.

Methods

Our retrospective feasibility study was based on a cohort of patients from a tertiary care hospital in Germany undergoing routine CEUS examination to evaluate malignancy of FLL. We trained a weakly supervised attention-based multiple instance learning algorithm

SUBMITTER: Luedde T 

PROVIDER: S-EPMC12680397 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature