Lite-FBCN：用于MRI图像脑疾病分类的轻量级快速双线性卷积网络
Lite-FBCN: Lightweight Fast Bilinear Convolutional Network for Brain Disease Classification from MRI Image
AI总结：
针对MRI脑疾病分类中精度与计算效率难平衡的问题，提出单网络架构的轻量级快速双线性卷积网络Lite-FBCN，通过通道缩减层压缩特征维度，在多项测试中准确率优于基线与现有双线性模型，适合资源受限或实时临床场景。
摘要：
在磁共振成像（MRI）扫描的脑疾病分类任务中，同时实现高精度与计算高效性颇具挑战，尤其是当粗粒度和细粒度区分都至关重要时。当前的深度学习方法往往难以在准确率与计算需求之间取得平衡。我们提出了Lite-FBCN，这是一种专为解决该问题设计的新型轻量级快速双线性卷积网络。与传统的双网络双线性模型不同，Lite-FBCN采用单网络架构，显著降低了计算负载。Lite-FBCN利用经过微调的轻量级预训练CNN（卷积神经网络）提取相关特征，并在双线性池化之前加入通道缩减层，最小化特征图维度，从而生成紧凑的双线性向量。在交叉验证和留出数据上开展的大量评估表明，Lite-FBCN不仅优于基线CNN，还超过了现有的双线性模型。搭载MobileNetV1的Lite-FBCN在交叉验证中达到98.10%的准确率，在留出数据上达到69.37%的准确率（较基线提升3%）。UMAP可视化进一步证实了其在区分高度相似的脑疾病类别方面的有效性。此外，Lite-FBCN在性能与计算效率之间实现了最优权衡，是提升资源受限或实时临床环境诊断能力的一种极具前景的解决方案。
英文摘要：
Achieving high accuracy with computational efficiency in brain disease classification from Magnetic Resonance Imaging (MRI) scans is challenging, particularly when both coarse and fine-grained distinctions are crucial. Current deep learning methods often struggle to balance accuracy with computational demands. We propose Lite-FBCN, a novel Lightweight Fast Bilinear Convolutional Network designed to address this issue. Unlike traditional dual-network bilinear models, Lite-FBCN utilizes a single-network architecture, significantly reducing computational load. Lite-FBCN leverages lightweight, pre-trained CNNs fine-tuned to extract relevant features and incorporates a channel reducer layer before bilinear pooling, minimizing feature map dimensionality and resulting in a compact bilinear vector. Extensive evaluations on cross-validation and hold-out data demonstrate that Lite-FBCN not only surpasses baseline CNNs but also outperforms existing bilinear models. Lite-FBCN with MobileNetV1 attains 98.10% accuracy in cross-validation and 69.37% on hold-out data (a 3% improvement over the baseline). UMAP visualizations further confirm its effectiveness in distinguishing closely related brain disease classes. Moreover, its optimal trade-off between performance and computational efficiency positions Lite-FBCN as a promising solution for enhancing diagnostic capabilities in resource-constrained and or real-time clinical environments.
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