Paper Info
Title
Versatile anomaly detection method for medical images with semi-supervised flow-based generative models
Abstract
Purpose Radiologists interpret many medical images and clinical practice demands timely interpretation, resulting in a heavy workload. To reduce the workload, here we formulate and validate a method that can handle different types of medical image and can detect virtually all types of lesion in a medical image. For the first time, we show that two flow-based deep generative (FDG) models can predict the logarithm posterior probability in a semi-supervised approach. Methods We adopt two FDG models in conjunction with Bayes' theorem to predict the logarithm posterior probability that a medical image is normal. We trained one of the FDG models with normal images and the other FDG model with normal and non-normal images. Results We validated the method using two types of medical image: chest X-ray images (CXRs) and brain computed tomography images (BCTs). The area under the receiver operating characteristic curve for pneumonia-like opacities in CXRs was 0.839 on average, and for infarction in BCTs was 0.904. Conclusion We formulated a method of predicting the logarithm posterior probability using two FDG models. We validated that the method can detect abnormal findings in CXRs and BCTs with both an acceptable performance for testing and a comparatively light workload for training.
Year
DOI
Venue
2021
10.1007/s11548-021-02480-4
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
Keywords
DocType
Volume
Anomaly detection, Brain computed tomography, Chest X-ray, Deep learning, Semi-supervised
Journal
16
Issue
ISSN
Citations 
12
1861-6410
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Hisaichi Shibata101.01
Shouhei Hanaoka201.01
Nomura, Y.3319.51
Takahiro Nakao432.49
Issei Sato500.34
Daisuke Sato632940.09
Naoto Hayashi700.68
Osamu Abe896.36