Auto-metric distribution propagation graph neural network with a meta-learning strategy for diagnosis of otosclerosis

Post time Yesterday 23:55

Authors:

Jiaoju Wang,
Jian Song,
Zheng Wang,
Shuang Mao,
Mengli Kong,
Yitao Mao,
Muzhou Hou &
Xuewen Wu

Otosclerosis is a multifactorial bone disorder that affects the otic capsule; otosclerosis is a significant cause of deafness in adults. Since the lesion areas are frequently subtle, the diagnosis of otosclerosis on temporal bone CT images tends to be difficult, especially for fenestral otosclerosis. We design a deep learning model for diagnosing otosclerosis on CT scans in the case of limited samples. That is, we design a dual graph network, namely, ADP-GNN, for predicting otosclerosis-positive and otosclerosis-negative samples; the network consists of point graphs and distribution graphs. More specifically, the point graph is used to model the instance-level relation between nodes, and the risk factors are integrated into it for multimodal diagnosis. The distribution graph is used to model the distribution-level relation between samples, and the copula function is introduced to better measure the dependency between nodes. The autometric strategy is also used to make the model more flexible and to enable the sample to be evaluated independently. Through the propagation between the two graphs and metatraining, the labels of unknown nodes can be predicted. Test experiments on otosclerosis datasets show that the performance of our model achieves accuracies of 98.15% and 97.69% for diagnosis in the left and right ears, respectively, and outperforms the other models. This verifies the advantage of our model in the case of limited samples. We also conduct experiments on a public dataset. The results demonstrate the stability of our model and that it achieves better performance when compared with existing studies. This work offers a new approach for the diagnosis of otosclerosis and facilitates the development of computer-aided diagnosis in clinical practice.

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