论文标题
在正确的位置寻找异常：可解释的AI通过自动位置学习
Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
论文作者
论文摘要
现在，深度学习已成为医学成像中识别异常情况的事实上的方法。他们将医学图像分类为异常标签的“黑匣子”方式为他们的接受带来了问题，尤其是在临床医生中。当前可解释的AI方法通过可视化（例如热图）提供了理由，但不能保证网络专注于完全包含异常的相关图像区域。在本文中，我们开发了一种可解释的AI的方法，在这种AI中，在存在时，请确保异常在预期位置重叠。通过从文本报告中自动提取特定于位置的标签，并使用双向长期短期记忆复发性神经网络（BI-LSTM）和densenet-121的混合组合来学习预期位置与标签的关联来实现这一目标。使用此预期位置将基于RESNET101的随后注意力引入的推理网络偏差导致在存在时在预期位置分离异常。该方法在大型胸部X射线数据集上进行评估。
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses problems for their acceptance, particularly with clinicians. Current explainable AI methods offer justifications through visualizations such as heat maps but cannot guarantee that the network is focusing on the relevant image region fully containing the anomaly. In this paper, we develop an approach to explainable AI in which the anomaly is assured to be overlapping the expected location when present. This is made possible by automatically extracting location-specific labels from textual reports and learning the association of expected locations to labels using a hybrid combination of Bi-Directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM) and DenseNet-121. Use of this expected location to bias the subsequent attention-guided inference network based on ResNet101 results in the isolation of the anomaly at the expected location when present. The method is evaluated on a large chest X-ray dataset.
