Name
Title | ||
|---|---|---|
Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TPAMI.2021.3130759 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Bayesian networks,deep neural networks,medical image analysis,neuro-probabilistic reasoning | Journal | 44 |
Issue | ISSN | Citations |
11 | 0162-8828 | 1 |
PageRank | References | Authors |
0.36 | 19 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gangming Zhao | 1 | 9 | 3.88 |
| Quanlong Feng | 2 | 1 | 0.36 |
| Chaoqi Chen | 3 | 1 | 0.36 |
| Zhen Zhou | 4 | 30 | 12.87 |
| Yizhou Yu | 5 | 2907 | 181.26 |