Optimizing MRI sequence classification performance: insights from domain shift analysis.

ABSTRACT:

Background

MRI sequence classification becomes challenging in multicenter studies due to variability in imaging protocols, leading to unreliable metadata and requiring labor-intensive manual annotation. While numerous automated MRI sequence identification models are available, they frequently encounter the issue of domain shift, which detrimentally impacts their accuracy. This study addresses domain shift, particularly from adult to pediatric MRI data, by evaluating the effectiveness of pre-trained models under these conditions.

Methods

This retrospective and multicentric study explored the efficiency of a pre-trained convolutional (ResNet) and CNN-Transformer hybrid model (MedViT) to handle domain shift. The study involved training ResNet-18 and MedVit models on an adult MR

SUBMITTER: Mahmutoglu MA 

PROVIDER: S-EPMC12559157 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature