提出高效多尺度注意力解码器，提升脑肿瘤分割精度与计算效率
Advanced Brain Tumor Segmentation Using EMCAD: Efficient Multi-scale Convolutional Attention Decoding
- 设计轻量级多尺度卷积注意力解码结构，降低计算开销
- 在BraTs2020上达0.285均值Dice，稳定无过拟合
- 适合资源受限场景下的医学图像分割应用
脑肿瘤分割是医学图像分析中的关键预处理步骤，需精确区分磁共振成像（MRI）中的肿瘤区域与健康脑组织。在计算资源有限的情况下，高效的解码机制尤为关键，但传统方法常伴随高计算成本。为此，本文提出EMCAD——一种新型高效多尺度卷积注意力解码器，用于优化脑肿瘤分割的性能与计算效率。模型在BraTs2020数据集（包含369名患者MRI扫描）上取得最佳Dice分数0.31，训练过程中保持稳定的平均Dice分数0.285 ± 0.015，表现中等但稳健，验证集上未出现过拟合现象。
原文摘要 · Abstract (English)
Brain tumor segmentation is a critical pre-processing step in the medical image analysis pipeline that involves precise delineation of tumor regions from healthy brain tissue in medical imaging data, particularly MRI scans. An efficient and effective decoding mechanism is crucial in brain tumor segmentation especially in scenarios with limited computational resources. However these decoding mechanisms usually come with high computational costs. To address this concern EMCAD a new efficient multi-scale convolutional attention decoder designed was utilized to optimize both performance and computational efficiency for brain tumor segmentation on the BraTs2020 dataset consisting of MRI scans from 369 brain tumor patients. The preliminary result obtained by the model achieved a best Dice score of 0.31 and maintained a stable mean Dice score of 0.285 plus/minus 0.015 throughout the training process which is moderate. The initial model maintained consistent performance across the validation set without showing signs of over-fitting.
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