Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Aug...Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.展开更多
随着深度学习的发展,基于CNN和Transformer的语义分割在遥感领域得到了广泛应用。然而,这些方法仍存在局限:前者缺乏远程建模能力,后者受制于计算复杂性。最近,Mamba所提出的视觉状态空间(visual state space,VSS)模型展现了其能够对远...随着深度学习的发展,基于CNN和Transformer的语义分割在遥感领域得到了广泛应用。然而,这些方法仍存在局限:前者缺乏远程建模能力,后者受制于计算复杂性。最近,Mamba所提出的视觉状态空间(visual state space,VSS)模型展现了其能够对远程关系进行有效线性计算的能力。受此启发,提出了一种基于CNN和视觉状态空间的遥感影像语义分割网络,以克服现有方法的局限。首先,构建一个由CNN和VSS分支组成的架构,并行提取多尺度特征信息,挖掘局部相关性并捕获远程上下文依赖关系,并将VSS代替Transformer应用于解码器;其次,设计了协同调制模块学习空间权重调制特征,以自适应融合双分支语义信息,增强语义信息间的依赖关系;最后,使用额外的辅助头优化网络,通过辅助损失函数引导模型在训练中更多关注关键区域。该方法在LoveDA和Vaihingen数据集上进行实验验证,其mF1指标分别为69.61%和90.53%,mIoU指标分别为53.95%和83.13%。实验结果表明,所提出的模型在这两个公共数据集上表现优于其他分割模型。展开更多
文摘Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.
文摘随着深度学习的发展,基于CNN和Transformer的语义分割在遥感领域得到了广泛应用。然而,这些方法仍存在局限:前者缺乏远程建模能力,后者受制于计算复杂性。最近,Mamba所提出的视觉状态空间(visual state space,VSS)模型展现了其能够对远程关系进行有效线性计算的能力。受此启发,提出了一种基于CNN和视觉状态空间的遥感影像语义分割网络,以克服现有方法的局限。首先,构建一个由CNN和VSS分支组成的架构,并行提取多尺度特征信息,挖掘局部相关性并捕获远程上下文依赖关系,并将VSS代替Transformer应用于解码器;其次,设计了协同调制模块学习空间权重调制特征,以自适应融合双分支语义信息,增强语义信息间的依赖关系;最后,使用额外的辅助头优化网络,通过辅助损失函数引导模型在训练中更多关注关键区域。该方法在LoveDA和Vaihingen数据集上进行实验验证,其mF1指标分别为69.61%和90.53%,mIoU指标分别为53.95%和83.13%。实验结果表明,所提出的模型在这两个公共数据集上表现优于其他分割模型。
