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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.22955 |
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| _version_ | 1866908427513495552 |
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| author | Nikkhah, Haniyeh Tanha, Jafar Zarrin, Mahdi Roshan, SeyedEhsan Kazempour, Amin |
| author_facet | Nikkhah, Haniyeh Tanha, Jafar Zarrin, Mahdi Roshan, SeyedEhsan Kazempour, Amin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22955 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | YM-WML: A new Yolo-based segmentation Model with Weighted Multi-class Loss for medical imaging Nikkhah, Haniyeh Tanha, Jafar Zarrin, Mahdi Roshan, SeyedEhsan Kazempour, Amin Computer Vision and Pattern Recognition 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. |
| title | YM-WML: A new Yolo-based segmentation Model with Weighted Multi-class Loss for medical imaging |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.22955 |