This paper introduces the three-branch Dual Attention-Guided Compact Bilinear CNN (DACB-Net) by focusing on learning from disease-specific regions to enhance accuracy and alignment. A global branch compensates for lost discriminative features, generating Attention Heat Maps (AHM) for relevant cropped regions. Finally, the last pooling layers of global and local branches are concatenated for fine-tuning, which offers a comprehensive solution to the challenges posed by skin disease diagnosis. Although current CNNs employ Stochastic Gradient Descent (SGD) for discriminative feature learning, using distinct pairs of local image patches to compute gradients and incorporating a modulation factor in the loss for focusing on complex data during training. However, this approach can lead to dataset imbalance, weight adjustments, and vulnerability to overfitting. The proposed solution combines two supervision branches and a novel loss function to address these issues, enhancing performance and interpretability. The framework integrates data augmentation, transfer learning, and fine-tuning to tackle data imbalance to improve classification performance, and reduce computational costs. Simulations on the HAM10000 and ISIC2019 datasets demonstrate the effectiveness of this approach, showcasing a 2.59% increase in accuracy compared to the state-of-the-art.

这篇论文介绍了一种名为DACB-Net的三支双重注意力引导紧凑双线性卷积神经网络，通过聚焦于学习疾病特定区域以提高准确性和对齐度。该网络结合来自全局分支和局部分支的最后池化层，通过微调提供了一种全面解决皮肤病诊断所面临挑战的方法。该解决方案结合了两个监督分支和一种新的损失函数，以应对数据不平衡、性能提升和可解释性问题。使用HAM10000和ISIC2019数据集的模拟实验证明了这种方法的有效性，相比于现有技术，准确性提高了2.59%。

DACB-Net：双注意引导的紧凑双线性卷积神经网络用于皮肤疾病分类