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
【目的】研究芦苇(Phragmites australis)湿地土地类型变化、监测覆被特征,为湿地保护和开发提供参考。【方法】基于2017—2023年9月扎龙湿地Sentinel-2遥感影像,制作了包含湖泊、芦苇地、建筑地、耕地、盐碱地5种土地类型的遥感影像数...【目的】研究芦苇(Phragmites australis)湿地土地类型变化、监测覆被特征,为湿地保护和开发提供参考。【方法】基于2017—2023年9月扎龙湿地Sentinel-2遥感影像,制作了包含湖泊、芦苇地、建筑地、耕地、盐碱地5种土地类型的遥感影像数据集。通过视觉状态空间(visual state space model,Vmamba)联合注意力机制并结合水体指数NDWI生成水体掩膜对研究区进行信息提取,统计各土地类型的位置和面积变化信息。利用像元二分法提取植被覆盖度(fractional vegetation cover,FVC),计算叶面积指数(leaf area index,LAI)和生态质量指数(ecosystem quality index,EQI)。【结果】通过本研究提出的方法对研究区内分布信息进行提取,整体精度(overall accuracy,OA)为80.85%、平均交并比(mean intersection over union,MIoU)为71.59%,宏观平均F1值(macro-F1,MF1)为79.93%。2017—2023年,在扎龙湿地内湖泊、芦苇地的覆盖面积呈增加趋势;耕地、建筑地的覆盖面积呈减少趋势;盐碱地的覆盖面积呈波动趋势。植被覆盖度、生态质量指数先升高后降低,与中国气候公报内容基本一致。【结论】Vmamba联合注意力机制并结合水体掩膜的模型,在湿地信息提取方面效果良好,一定程度上提高土地利用分类与变化监测的精度。植被覆盖度、叶面积指数、生态质量指数的监测对湿地资源管理与可持续利用提供借鉴。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【目的】研究芦苇(Phragmites australis)湿地土地类型变化、监测覆被特征,为湿地保护和开发提供参考。【方法】基于2017—2023年9月扎龙湿地Sentinel-2遥感影像,制作了包含湖泊、芦苇地、建筑地、耕地、盐碱地5种土地类型的遥感影像数据集。通过视觉状态空间(visual state space model,Vmamba)联合注意力机制并结合水体指数NDWI生成水体掩膜对研究区进行信息提取,统计各土地类型的位置和面积变化信息。利用像元二分法提取植被覆盖度(fractional vegetation cover,FVC),计算叶面积指数(leaf area index,LAI)和生态质量指数(ecosystem quality index,EQI)。【结果】通过本研究提出的方法对研究区内分布信息进行提取,整体精度(overall accuracy,OA)为80.85%、平均交并比(mean intersection over union,MIoU)为71.59%,宏观平均F1值(macro-F1,MF1)为79.93%。2017—2023年,在扎龙湿地内湖泊、芦苇地的覆盖面积呈增加趋势;耕地、建筑地的覆盖面积呈减少趋势;盐碱地的覆盖面积呈波动趋势。植被覆盖度、生态质量指数先升高后降低,与中国气候公报内容基本一致。【结论】Vmamba联合注意力机制并结合水体掩膜的模型,在湿地信息提取方面效果良好,一定程度上提高土地利用分类与变化监测的精度。植被覆盖度、叶面积指数、生态质量指数的监测对湿地资源管理与可持续利用提供借鉴。
