Medical image anomaly detection based on diffusion ordinary differential equations
To identify abnormal medical images that deviate significantly from normal physiological states,a medical image anomaly detection method,which is based on diffusion ordinary differential equations(ODE)assisted by neural network,is proposed according to the assumption that the relevant features of abnormal images often appear in low-density regions of the feature distribution.Firstly,diffusion ODE is utilized to estimate the likelihood value of the image features;then,a neural network is constructed to fit the likelihood values of the image features at different times estimated by the diffusion ODE;finally,the anomaly score of this method is the weighted average of the likelihood values estimated by the diffusion ODE and the likelihood values estimated by the neural network.Images with high anomaly scores are identified as abnormal images.In addition,an anomaly localization method based on image reconstruction is proposed to determine the abnormal regions of the abnormal images,and the reconstruction errors are calculated to locate the abnormal regions.The numerical experimental results on the BraTS2021 brain tumor dataset and the chest X-ray dataset show that the anomaly detection performance of this method is significantly better than that of other existing methods and has preferable robustness.The approach for unsupervised medical image anomaly detection and the method to locate the abnormal regions proposed in this article can provide mass information support for clinical diagnosis and treatment,and are expected to reduce the workload of doctors.
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