Image classification using convolutional neural networks (CNNs) outperforms other state-of-the-art methods. Moreover, attention can be visualized as a heatmap to improve the explainability of results of a CNN. We designed a framework that can generate heatmaps reflecting lesion regions precisely. We generated initial heatmaps by using a gradient-based classification activation map (Grad-CAM). We assume that these Grad-CAM heatmaps correctly reveal the lesion regions; then we apply the attention mining technique to these heatmaps to obtain integrated heatmaps. Moreover, we assume that these Grad-CAM heatmaps incorrectly reveal the lesion regions and design a dissimilarity loss to increase their discrepancy with the Grad-CAM heatmaps. In this study, we found that having professional ophthalmologists select 30% of the heatmaps covering the lesion regions led to better results, because this step integrates (prior) clinical knowledge into the system. Furthermore, we design a knowledge preservation loss that minimizes the discrepancy between heatmaps generated from the updated CNN model and the selected heatmaps. Experiments using fundus images revealed that our method improved classification accuracy and generated attention regions closer to the ground truth lesion regions in comparison with existing methods.

中文翻译：

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如何以更少的负担提取更多信息：眼科医生干预眼底图像分类和视网膜疾病定位。

使用卷积神经网络（CNN）进行图像分类的性能优于其他最新技术。此外，可以将注意力可视为热图，以提高CNN结果的可解释性。我们设计了一个框架，可以生成精确反映病变区域的热图。我们使用基于梯度的分类激活图（Grad-CAM）生成了初始热图。我们假设这些Grad-CAM热图正确显示了病变区域。然后将注意力挖掘技术应用于这些热图以获得集成的热图。此外，我们假设这些Grad-CAM热图错误地显示了病变区域，并设计了相异度损失以增加其与Grad-CAM热图的差异。在这个研究中，我们发现，由专业眼科医生选择覆盖病变区域的热图的30％可获得更好的结果，因为此步骤将（先前的）临床知识整合到系统中。此外，我们设计了一种知识保存损失，可最大程度地减少从更新的CNN模型生成的热图与所选热图之间的差异。使用眼底图像进行的实验表明，与现有方法相比，我们的方法提高了分类准确性，并生成了更靠近地面真相病变区域的关注区域。