BMSMM-Net:基于Mamba和多视角提取的骨转移细分框架
Fudong Shang1, Shouguo Tang1, Xiaorong Wan1
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan Key Laboratory of Computer Technologies Application, Kunming, China (F.S., S.T., X.W., Y.L., L.W.).
本研究介绍了BMSMM-Net,这是一种用于精确骨转移细分的深度学习框架. 它显著提高了检测骨转移的准确性和效率,有助于患者的护理.
科学领域:
- 医学成像分析分析 医学成像分析
- 人工智能在瘤学中的应用
- 深度学习用于医学诊断.
背景情况:
- 转移性骨瘤严重影响患者的生活质量和癌症进展.
- 目前的手动细分方法耗时且主观.
- 精确细分各种骨病变对于改善患者的治疗结果至关重要.
研究的目的:
- 开发一种新的深度学习框架,用于精确有效地对骨转移进行细分.
- 为了增强检测骨质细胞,骨质溶解和混合骨损伤.
- 改进现有的骨转移细分方法.
主要方法:
- 介绍了BMSMM-Net,这是骨转移的新型细分框架.
- 集成的瓶门Mamba (BGM) 和Skip-Mamba (SKM) 模块,以增强功能依赖性和融合.
- 采用多视角提取 (MPE) 模块,具有多种卷积内核,以提高灵敏度.
主要成果:
- 在BM-Seg数据集上实现了高性能,骨转移的F1得分为91.07%,骨区域的F1得分为95.17%.
- 获得的mIoU得分为骨转移的83.60%和骨区域的90.78%.
- 与现有模型相比,证明了优越的细分精度和计算效率.
结论:
- 通过使用BGM,SKM和MPE模块,BMSMM-Net有效地解决了骨转移细分方面的挑战.
- 该框架提供了更高的准确性,并优于当前先进的方法.
- BMSMM-Net的效率和准确性使其适用于检测骨转移的临床应用.
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