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Abstract #0378

Discriminative Feature Learning for Lacune Detection in 2D T2-FLAIR Images using Supervised Contrastive Learning

Sae Hyun Kim1, Chong Hyun Suh2, Min Woo Han3, Wooseok Jung1, and Seung Hyun Lee1
1VUNO Inc., Seoul, Korea, Republic of, 2Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Korea, Republic of, 3University of Ulsan College of Medicine, Seoul, Korea, Republic of

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, cSVD, Lacunes, MRI

Motivation: Current deep learning approaches struggle to distinguish lacunar infarcts from similar structures in cerebral small vessel disease.

Goal(s): To develop an automated method that can effectively differentiate lacunes from similar-appearing structures (perivascular spaces, vessels) in imbalanced datasets.

Approach: We trained a ResNet-34 encoder using supervised contrastive learning for enhanced feature discrimination, with Attention U-Net as the downstream segmentation network, using 427 FLAIR scans with expert annotations.

Results: This work demonstrates an effective encoder training strategy for distinguishing small lesions like lacunes in cerebral small vessel disease through enhanced feature discrimination, potentially reducing both radiological interpretation time and inter-reader variability.

Impact: This work demonstrates an effective encoder training strategy for distinguishing small lesions like lacunes in cerebral small vessel disease through enhanced feature discrimination, potentially reducing both radiological interpretation time and inter-reader variability.

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Keywords