YOLO改进模型+加权损失，提升心脏图像分割精度
YM-WML: A new Yolo-based segmentation Model with Weighted Multi-class Loss for medical imaging
- 基于YOLOv11结构，融合注意力机制增强特征提取
- 在ACDC数据集上达91.02%的Dice系数，优于现有方法
- 适合医学图像中类别不平衡场景的精准分割任务
由于医学图像存在类别不平衡和结构复杂等问题，图像分割面临挑战。本文提出一种新型心脏图像分割模型YM-WML，整合了强大的骨干网络以实现有效特征提取，采用YOLOv11颈部进行多尺度特征融合，并设计基于注意力的分割头以实现精确分割。为缓解类别不平衡问题，引入加权多类指数（WME）损失函数。在ACDC数据集上，YM-WML达到91.02%的骰子相似系数，优于当前先进方法。该模型表现出稳定的训练过程、高精度分割能力与强泛化性能，为心脏图像分割任务树立了新基准。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。