Chronic Gastritis Detection from Gastric X-ray Images via Deep Autoencoding Gaussian Mixture Models

This paper presents a detection method of chronic gastritis from gastric X-ray images. The conventional method cannot detect chronic gastritis accurately since the number of non-gastritis images is overwhelmingly larger than the number of gastritis images. To deal with this problem, the proposed method performs the detection of chronic gastritis by using Deep Autoencoding Gaussian Mixture Models (DAGMM) which is an anomaly detection approach. DAGMM enables construction of chronic gastritis detection model using only non-gastritis images. In addition, DAGMM is superior to conventional anomaly detection methods since the models of dimensionality reduction and density estimation can be learned simultaneously. Therefore, the proposed method realizes accurate detection of chronic gastritis by utilizing DAGMM.

[1]  Thomas S. Huang,et al.  One-class SVM for learning in image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[2]  Antonio Criminisi,et al.  Segmentation of Brain Tumor Tissues with Convolutional Neural Networks , 2014 .

[3]  P. Sipponen,et al.  Helicobacter pylori infection and chronic gastritis in gastric cancer. , 1992, Journal of clinical pathology.

[4]  Xiaogang Wang,et al.  Medical image classification with convolutional neural network , 2014, 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).

[5]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[6]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[7]  Bo Zong,et al.  Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.

[8]  N. Yamamichi,et al.  Atrophic gastritis and enlarged gastric folds diagnosed by double-contrast upper gastrointestinal barium X-ray radiography are useful to predict future gastric cancer development based on the 3-year prospective observation , 2016, Gastric Cancer.

[9]  Min Chen,et al.  Deep Learning for Imbalanced Multimedia Data Classification , 2015, 2015 IEEE International Symposium on Multimedia (ISM).

[10]  Miki Haseyama,et al.  Detection of gastric cancer risk from X-ray images via patch-based convolutional neural network , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[11]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).