MRISeqClassifier: A Deep Learning Toolkit for Precise MRI Sequence Classification.

Journal: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Published Date: Jun 10, 2025

Abstract

Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool in medicine, widely used to detect and assess various health conditions. Different MRI sequences, such as T1-weighted, T2-weighted, and FLAIR, serve distinct roles by highlighting different tissue characteristics and contrasts. However, distinguishing them based solely on the description file is currently impossible due to confusing or incorrect annotations. Additionally, there is a notable lack of effective tools to differentiate these sequences. In response, we developed a deep learning-based toolkit tailored for small, unrefined MRI datasets. This toolkit enables precise sequence classification and delivers performance comparable to systems trained on large, meticulously curated datasets. Utilizing lightweight model architectures and incorporating a voting ensemble method, the toolkit enhances accuracy and stability. It achieves a 99% accuracy rate using only 10% of the data typically required in other research. The code is available at https://github.com/JinqianPan/MRISeqClassifier.

Authors

  • Jinqian Pan
    Center for Data Science, New York University, 60 5th Ave, New York, NY, 10011, USA.
  • Qi Chen
    Department of Gastroenterology, Jining First People's Hospital, Jining, China.
  • Chengkun Sun
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 1889 Museum Road, Office 7020, Gainesville, FL, 32611, United States, 1 3526279467.
  • Renjie Liang
    Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA.
  • Jiang Bian
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America.
  • Jie Xu
    Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310000, China.

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