High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning method for chronic gastritis classification using gastric X-ray images. The proposed semi-supervised learning method based on tri-training can leverage unannotated data to boost the performance that is achieved with a small amount of annotated data. We utilize a novel learning method named Between-Class learning (BC learning) that can considerably enhance the performance of our semi-supervised learning method. As a result, our method can effectively learn from unannotated data and achieve high diagnostic accuracy for chronic gastritis.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Bai W, Oktay O, Sinclair M, Suzuki H, Rajchl M, Tarroni G, Glocker B, King A, Matthews PM, Rueckert D (2017) Semi-supervised learning for network-based cardiac mr image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 253–260
Baur C, Albarqouni S, Navab N (2017) Semi-supervised deep learning for fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 311–319
Bechar MEA, Settouti N, Barra V, Chikh MA (2018) Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease. Multidim Syst Sign Process 29(3):979–998
Breiman L (2001) Random forests. Machine Learning 45(1):5–32
Chen G, Zhang J, Zhuo D, Pan Y, Pang C (2019) Identification of pulmonary nodules via ct images with hierarchical fully convolutional networks. Med Biol Eng Comput, pp 1–14
Cortes C, Vapnik V (1995) Support-vector networks. Machine Learning 20(3):273–297
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255
Dheer S, Levine M, Redfern R, Metz D, Rubesin S, Laufer I (2002) Radiographically diagnosed antral gastritis: findings in patients with and without helicobacter pylori infection. The British Journal of Radiology 75(898):805–811
Efron B (1992) Bootstrap methods: another look at the jackknife. In: Breakthroughs in statistics. Springer, pp 569–593
Fisher RA (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2):179–188
Han G, Liu X, Zheng G, Wang M, Huang S (2018) Automatic recognition of 3d ggo ct imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning cnns. Med Biol Eng Comput 56(12):2201–2212
Hatipoglu N, Bilgin G (2017) Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med Biol Eng Comput 55(10):1829–1848
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: CVPR, vol 1 , p 3
Kimura K, Takemoto T (1969) An endoscopic recognition of the atrophic border and its significance in chronic gastritis. Endoscopy 1(03):87–97
Kudo T, Kakizaki S, Sohara N, Onozato Y, Okamura S, Inui Y, Mori M (2011) Analysis of abc (d) stratification for screening patients with gastric cancer. World Journal of Gastroenterology: WJG 17(43):4793
LeCun Y, Bengio Y, Hinton G (2015) Deep Learning. Nature 521(7553):436
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Medical Image Analysis 42:60–88
Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE, pp 565–571
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234–241
Setio AAA, Traverso A, De Bel T, Berens MS, van den Bogaard C, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B, et al. (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Medical Image Analysis 42:1–13
She Q, Hu B, Luo Z, Nguyen T, Zhang Y (2019) A hierarchical semi-supervised extreme learning machine method for eeg recognition. Med Biol Eng Comput 57(1):147–157
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging 35(5):1285–1298
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
Togo R, Ishihara K, Ogawa T, Haseyama M (2016) Estimation of salient regions related to chronic gastritis using gastric x-ray images. Computers in Biology and Medicine 77:9–15
Togo R, Yamamichi N, Mabe K, Takahashi Y, Takeuchi C, Kato M, Sakamoto N, Ishihara K, Ogawa T, Haseyama M (2018) Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium x-ray radiography. Journal of Gastroenterology, pp 1–9
Tokozume Y, Ushiku Y, Harada T (2018) Between-class learning for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5486–5494
Vivanti R, Joskowicz L, Lev-Cohain N, Ephrat A, Sosna J (2018) Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up ct studies. Med Biol Eng Comput 56(9):1699–1713
Yamamichi N, Hirano C, Ichinose M, Takahashi Y, Minatsuki C, Matsuda R, Nakayama C, Shimamoto T, Kodashima S, Ono S, et al. (2016) 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. Gastric Cancer 19(3):1016–1022
Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2017) Mixup: Beyond empirical risk minimization. arXiv:1710.09412
Zhang Y, Yang L, Chen J, Fredericksen M, Hughes DP, Chen DZ (2017) Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 408–416
Zhou ZH, Li M (2005) Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17(11):1529–1541
Zhou ZH, Li M (2010) Semi-supervised learning by disagreement. Knowl Inf Syst 24(3):415–439
The clinical data were acquired at The University of Tokyo Hospital in Japan. We express our thanks to Katsuhiro Mabe of the Junpukai Health Maintenance Center and Nobutake Yamamichi of The University of Tokyo Hospital.
This study was partly supported by JSPS KAKENHI Grant number JP17H01744.
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Li, Z., Togo, R., Ogawa, T. et al. Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training. Med Biol Eng Comput 58, 1239–1250 (2020). https://doi.org/10.1007/s11517-020-02159-z
- Chronic gastritis
- Computer-aided diagnosis
- Medical image analysis
- Convolutional neural network
- Semi-supervised learning