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arXiv · 2506.22955

YM-WML: A new Yolo-based segmentation Model with Weighted Multi-class Loss for medical imaging

Haniyeh Nikkhah·Jafar Tanha·Mahdi Zarrin·SeyedEhsan Roshan·Amin Kazempour
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Abstract

Medical image segmentation poses significant challenges due to class imbalance and the complex structure of medical images. To address these challenges, this study proposes YM-WML, a novel model for cardiac image segmentation. The model integrates a robust backbone for effective feature extraction, a YOLOv11 neck for multi-scale feature aggregation, and an attention-based segmentation head for precise and accurate segmentation. To address class imbalance, we introduce the Weighted Multi-class Exponential (WME) loss function. On the ACDC dataset, YM-WML achieves a Dice Similarity Coefficient of 91.02, outperforming state-of-the-art methods. The model demonstrates stable training, accurate segmentation, and strong generalization, setting a new benchmark in cardiac segmentation tasks.

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Haniyeh Nikkhah, Jafar Tanha, Mahdi Zarrin, SeyedEhsan Roshan, Amin Kazempour. 2025-06-28. YM-WML: A new Yolo-based segmentation Model with Weighted Multi-class Loss for medical imaging. https://arxiv.org/abs/2506.22955

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