With the development of Artificial Intelligence, the auxiliary diagnosis model based on deep learning can assist doctors to a certain extent. However, the latent information in medical images, such as lesion features, is ignored in most of the traditional methods. The extraction of this information is regarded as a learning task within the network in some recent researches, but it requires a large amount of fine-labeled data, which is undoubtedly expensive. In response to the problem above, this paper proposes an Adversarial Lesion Enhancement Neural Network for Medical Image Classification (ALENN), which is used to locate and enhance the lesion information in medical images only under weakly annotated data so as to improve the accuracy of the auxiliary diagnosis model. This method is a two-stage framework, including a structure-based lesion adversarial inpainting module and a lesion enhancement classification module. The first stage is used to repair the lesion area in the images while the second stage is used to locate the lesion area and use the lesion enhanced data during modeling process. In the end, we verified the effectiveness of our method on the MURA dataset, a musculoskeletal X-ray dataset released by Stanford University. Experimental results show that our method can not only locate the lesion area but also improve the effectiveness of the auxiliary diagnosis model.

中文翻译：

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基于对抗性病变增强的医学图像分类模型

随着人工智能的发展，基于深度学习的辅助诊断模型可以在一定程度上辅助医生。然而，大多数传统方法都忽略了医学图像中的潜在信息，例如病变特征。在最近的一些研究中，提取这些信息被视为网络内部的学习任务，但它需要大量精细标记的数据，这无疑是昂贵的。针对上述问题，本文提出了一种用于医学图像分类的对抗性病灶增强神经网络（ALENN），用于仅在弱标注数据下定位和增强医学图像中的病灶信息，以提高分类准确率。辅助诊断模型。这种方法是一个两阶段的框架，包括基于结构的病变对抗修复模块和病变增强分类模块。第一阶段用于修复图像中的病变区域，而第二阶段用于定位病变区域并在建模过程中使用病变增强数据。最后，我们在斯坦福大学发布的肌肉骨骼 X 射线数据集 MURA 数据集上验证了我们的方法的有效性。实验结果表明，我们的方法不仅可以定位病变区域，还可以提高辅助诊断模型的有效性。我们在斯坦福大学发布的肌肉骨骼 X 射线数据集 MURA 数据集上验证了我们的方法的有效性。实验结果表明，我们的方法不仅可以定位病变区域，还可以提高辅助诊断模型的有效性。我们在斯坦福大学发布的肌肉骨骼 X 射线数据集 MURA 数据集上验证了我们的方法的有效性。实验结果表明，我们的方法不仅可以定位病变区域，还可以提高辅助诊断模型的有效性。