XiaoMi-AI文件搜索系统
World File Search Systeminspired
Cascade Residual Multiscale Convolution and Mamba- Structured UNet for Advanced Brain Tumor Image Segmentation
Abstract: In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model's potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.
儿童亲密友谊、心理健康和认知之间非线性关联的大脑和分子机制
1 复旦大学类脑智能科学与技术研究所，上海，中国；2 计算神经科学与类脑智能教育部重点实验室（复旦大学），上海，中国；3 教育部医学神经生物学国家重点实验室和脑科学教育部前沿研究中心（复旦大学），上海，中国；4 英国考文垂华威大学计算机科学系；5 英国牛津计算神经科学中心；6 英国剑桥大学精神病学系；7 英国剑桥大学行为与临床神经科学研究所；8 浙江师范大学复旦 ISTBI—ZJNU 类脑智能算法中心，金华，中国；9 上海医学院和中山医院免疫治疗技术转移中心，上海，中国；10 复旦大学华山医院神经内科，上海，中国； 11 张江复旦国际创新中心，上海，中国
COL333/671：人工智能简介
5 受此示例启发：https://www.shell.in/energy-and-innovation/ai-hackathon/_jcr_content/par/textimage_1834506119_1619963074.stream/1612943059963/4b0a86b7cc0fe7179148284ffed9ef33524c2816/windfarm-layout-optimisation-challenge.pdf