2022/08/19 by Gangming Zhao, Quanlong Feng, Zhao, Gangming +7 · 3 citations
Computer Science · Mathematics · Medicine · #AI in cancer detection #Artificial intelligence #Artificial neural network #Bayesian network #Benchmark (surveying) #COVID-19 diagnosis using AI #Computer science #Convolutional neural network #Feature (linguistics) #Generalization #Image (mathematics) #Machine learning #Mathematics #Pattern recognition (psychology) #Probabilistic logic #Probabilistic neural network #Radiomics and Machine Learning in Medical Imaging #Time delay neural network #cs.CV
paper · pdf · doi:10.48550/arxiv.2208.09282
published in arXiv (Cornell University) (Cornell University) · To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
arxiv created 2022/08/19 · openalex publication_date 2022/08/19 · arxiv updated 2022/08/22 · openalex created_date 2022/08/23 · openalex updated_date 2026/08/08
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.