A clinically applicable deep-learning model for automatic detection of focal liver lesions on Gd-DTPA-enhanced MRI

This is the authors' abstract. We don't add key points for this paper.

Accurate detection of focal liver lesions in Gd-DTPA-enhanced magnetic resonance imaging (MRI) necessitates a high level of skill and experience. This task is typically performed by radiologists through visual inspection, which is time-consuming, labor-intensive, and subject to intra- and inter-observer variation. Convolutional neural networks (CNNs) have demonstrated significant potential in detecting lesions on medical imaging. Our study presents a unified multi-sequence lesion detector model for automatically detecting focal liver lesions on Gd-DTPA-enhanced MR images to aid in treatment decision-making.