Comparative analysis of three advanced deep learning algorithms for Multiple Sclerosis lesion segmentation in FLAIR MRI - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Comparative analysis of three advanced deep learning algorithms for Multiple Sclerosis lesion segmentation in FLAIR MRI

Résumé

This paper addresses the pressing need for enhanced tools in the diagnosis and management of Multiple Sclerosis (MS), particularly in the accurate detection and segmentation of MS lesions. Leveraging recent advances in deep learning, we evaluate the performance of three state-of-the-art algorithms, focusing on their potential to improve both precision and efficiency in MS lesion segmentation from medical images. Our study provides critical insights into the strengths and limitations of each model, offering valuable guidance for future applications of AI in MS diagnosis and treatment.
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Dates et versions

hal-04767216 , version 1 (05-11-2024)

Identifiants

  • HAL Id : hal-04767216 , version 1

Citer

Yi Zhu, Thomas Grenier, C. Revol-Muller. Comparative analysis of three advanced deep learning algorithms for Multiple Sclerosis lesion segmentation in FLAIR MRI. IEEE ICSP’24 & 5th Sino-French Workshop 2024 “Medical Image Analysis and AI (MAI)”, IEEE Beijing Section, Beijing Jiaotong University, and Soochow University, Oct 2024, Suzhou, China, France. ⟨hal-04767216⟩
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