Anomaly detection is an important task for medical image analysis, which can alleviate the reliance of supervised methods on large labelled datasets. Most existing methods use a pixel-wise self-reconstruction framework for anomaly detection. However, there are two challenges of these studies: 1) they tend to overfit learning an identity mapping between the input and output, which leads to failure in detecting abnormal samples; 2) the reconstruction considers the pixel-wise differences which may lead to an undesirable result. To mitigate the above problems, we propose a novel heterogeneous Auto-Encoder (Hetero-AE) for medical anomaly detection. Our model utilizes a convolutional neural network (CNN) as the encoder and a hybrid CNN-Transformer network as the decoder. The heterogeneous structure enables the model to learn the intrinsic information of normal data and enlarge the difference on abnormal samples. To fully exploit the effectiveness of Transformer in the hybrid network, a multi-scale sparse Transformer block is proposed to trade off modelling long-range feature dependencies and high computational costs. Moreover, the multi-stage feature comparison is introduced to reduce the noise of pixel-wise comparison. Extensive experiments on four public datasets (i.e., retinal OCT, chest X-ray, brain MRI, and COVID-19) verify the effectiveness of our method on different imaging modalities for anomaly detection. Additionally, our method can accurately detect tumors in brain MRI and lesions in retinal OCT with interpretable heatmaps to locate lesion areas, assisting clinicians in diagnosing abnormalities efficiently.

中文翻译：

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使用异构自动编码器进行医学图像异常检测

异常检测是医学图像分析的一项重要任务，它可以减轻监督方法对大型标记数据集的依赖。大多数现有方法使用逐像素自重建框架来进行异常检测。然而，这些研究存在两个挑战：1）它们往往会过度拟合学习输入和输出之间的恒等映射，从而导致无法检测异常样本； 2）重建考虑了可能导致不良结果的像素差异。为了缓解上述问题，我们提出了一种用于医疗异常检测的新型异构自动编码器（Hetero-AE）。我们的模型利用卷积神经网络 (CNN) 作为编码器，使用混合 CNN-Transformer 网络作为解码器。异构结构使模型能够学习正常数据的内在信息并放大异常样本的差异。为了充分利用 Transformer 在混合网络中的有效性，提出了一种多尺度稀疏 Transformer 块来权衡建模远程特征依赖性和高计算成本。此外，引入多级特征比较来减少逐像素比较的噪声。对四个公共数据集（即视网膜 OCT、胸部 X 光、脑 MRI 和 COVID-19）的广泛实验验证了我们的方法在不同成像模式上进行异常检测的有效性。此外，我们的方法可以通过可解释的热图准确检测脑 MRI 中的肿瘤和视网膜 OCT 中的病变，以定位病变区域，帮助临床医生有效诊断异常。