During clinical practice, radiologists often use attributes, e.g. morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attributes as well as all relationships involving attributes could boost the generalization ability and verifiability of medical image diagnosis algorithms. In this paper, we introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. There are two parallel branches in our hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning and a graph convolutional network branch performing more generic relational modeling and reasoning using a feature representation. Tight coupling between these two branches is achieved via a cross-network attention mechanism and the fusion of their classification results. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks. On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95.36\% and an AUC of 96.54\%. Our method also achieves a 3.24\% accuracy improvement on an in-house chest X-ray image dataset for tuberculosis diagnosis. Our ablation study indicates that our hybrid algorithm achieves a much better generalization performance than a pure neural network architecture under very limited training data.
翻译:在临床实践期间,放射科医生经常使用病菌形态学和外貌特征等特征来帮助疾病诊断。有效的模型化特征以及所有涉及属性的关系可以提高医学图像诊断算法的普及能力和可核查性。在本文中,我们引入了一种混合神经概率推算算算法,用于可核查的基于属性的医学图像诊断。我们的混合算法中有两个平行分支:一个巴伊西亚网络分支,进行概率性因果关系推理,一个图形共生网络分支,使用特征表示法,进行更通用的关系模型和推理。这两个分支之间通过跨网络关注机制及其分类结果的融合而实现紧密结合。我们成功地将混合推算法应用于两项具有挑战性的医疗图像诊断任务。在LIDC-IDRI基准数据集中,用于对CT图像中的肺脏结核进行良性混合分类,我们的方法实现了95.36 ⁇ 和96-5 ⁇ 的AUC。我们的方法还实现了对内部混合基因分析的3.24-精确度改进了我们公司内部的胸腔分析模型的精度分析。