ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation -- Methods and Results
원문 초록
This report summarizes the outcomes of the ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation (MSLesSeg). The competition aimed to develop methods capable of automatically segmenting multiple sclerosis lesions in MRI scans. Participants were provided with a novel annotated dataset comprising a heterogeneous cohort of MS patients, featuring both baseline and follow-up MRI scans acquired at different hospitals. MSLesSeg focuses on developing algorithms that can independently segment multiple sclerosis lesions of an unexamined cohort of patients. This segmentation approach aims to overcome current benchmarks by eliminating user interaction and ensuring robust lesion detection at different timepoints, encouraging innovation and promoting methodological advances.
함께 살펴볼 논문
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How We Won the ISLES'24 Challenge by Preprocessing
Integrating Radiomics with Deep Learning Enhances Multiple Sclerosis Lesion Delineation
FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework
ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation with Zero-Shot Method
토론
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