一种肿瘤感知的密集网络Swin混合学习框架，结合增强和分层特征空间用于大规模脑部MRI分类
A Tumor Aware DenseNet Swin Hybrid Learning with Boosted and Hierarchical Feature Spaces for Large-Scale Brain MRI Classification
AI总结：
本文提出了一种结合增强和分层特征空间的DenseNet-Swin混合学习框架，用于大规模脑MRI肿瘤分类，通过定制DenseNet和Swin模块提升检测精度和召回率。
摘要：
本研究提出了一种高效的密集Swin混合（EDSH）框架，用于脑肿瘤MRI分析，旨在联合捕捉细粒度纹理模式和长距离上下文依赖性。介绍了两种肿瘤感知的实验设置，以解决特定类别的诊断挑战。第一种设置采用增强特征空间（BFS），其中独立定制的DenseNet和Swin分支学习互补的局部和全局表示，这些表示在维度上对齐、融合和增强，从而通过成功学习不规则形状、边界不清晰的肿块和异质纹理特征，实现了对弥漫性胶质瘤模式的高灵敏度检测。第二种设置采用分层DenseNet Swin架构，具有深度特征提取和双残差连接（DFE和DR），其中DenseNet作为结构化局部特征学习的CNN茎部，而Swin_t模型全局肿瘤形态，通过学习边界清晰的肿块、位置（在脑外）和肿瘤扩张（硬膜尾或向上延伸）的特征，有效抑制了脑膜瘤和垂体肿瘤分类中的假阴性。DenseNet在输入级别进行定制，以匹配MRI空间特性，利用密集残差连接来保留纹理信息并缓解消失梯度效应。同时，Swin_t通过任务对齐的补丁嵌入和移位窗口自注意力机制进行定制，以高效捕捉分层的全局依赖性。在大规模MRI数据集（40,260张图像跨越四个肿瘤类别）上的广泛评估表明，该框架在独立CNN、视觉Transformer和混合模型上均表现出一致的优越性，在测试未见过的数据集上实现了98.50的准确率和召回率。
英文摘要：
This study proposes an efficient Densely Swin Hybrid (EDSH) framework for brain tumor MRI analysis, designed to jointly capture fine grained texture patterns and long range contextual dependencies. Two tumor aware experimental setups are introduced to address class-specific diagnostic challenges. The first setup employs a Boosted Feature Space (BFS), where independently customized DenseNet and Swint branches learn complementary local and global representations that are dimension aligned, fused, and boosted, enabling highly sensitive detection of diffuse glioma patterns by successfully learning the features of irregular shape, poorly defined mass, and heterogeneous texture. The second setup adopts a hierarchical DenseNet Swint architecture with Deep Feature Extraction have Dual Residual connections (DFE and DR), in which DenseNet serves as a stem CNN for structured local feature learning, while Swin_t models global tumor morphology, effectively suppressing false negatives in meningioma and pituitary tumor classification by learning the features of well defined mass, location (outside brain) and enlargments in tumors (dural tail or upward extension). DenseNet is customized at the input level to match MRI spatial characteristics, leveraging dense residual connectivity to preserve texture information and mitigate vanishing-gradient effects. In parallel, Swint is tailored through task aligned patch embedding and shifted-window self attention to efficiently capture hierarchical global dependencies. Extensive evaluation on a large-scale MRI dataset (stringent 40,260 images across four tumor classes) demonstrates consistent superiority over standalone CNNs, Vision Transformers, and hybrids, achieving 98.50 accuracy and recall on the test unseen dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
