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Abstract #1861

Improving Brain Tumor Segmentation with a Clinically-Informed Multi-Decoder U-Net

Abbas Mohamed Rezk1, Abdulkhalek Al-Fakih1, Abdulla Shazly1, Kanghyun Ryu2, and Mohammed A. Al-masni1
1Department of Artificial Intelligence and Data Science, College of Artificial Intelligence Convergence, Sejong University, Seoul, Korea, Republic of, 2Artificial Intelligence and Robotics Institute, Institute of Science and Technology, Seoul, Korea, Republic of

Synopsis

Keywords: Diagnosis/Prediction, Segmentation

Motivation: Advancements in deep learning have improved brain tumor segmentation, but many methods do not leverage clinically relevant input modalities for specific tumor subtypes.

Goal(s): We aimed to develop a multi-decoder U-Net architecture to enhance segmentation accuracy by integrating modality-specific MRI sequences.

Approach: Our approach utilized a shared encoder branching into three decoders, each tailored to a tumor region, incorporating guided squeeze-and-excitation attention blocks for optimal feature integration.

Results: Evaluated on multiple datasets, our model showed significant improvements in Dice scores—up to 2.5% on the BraTS Africa dataset—demonstrating robustness and generalizability.

Impact: Our clinically guided, multi-decoder U-Net demonstrates improved segmentation accuracy, particularly in diverse and non-standard datasets. This innovation paves the way for more reliable, adaptable, and interpretable brain tumor imaging, enhancing diagnostic confidence and treatment planning.

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