Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Medical & Biological Engineering & Computing
Erschienen in: Medical & Biological Engineering & Computing 6/2020

27.03.2020 | Original Article

Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training

verfasst von: Zongyao Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 6/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.
Vorheriger Artikel Transfer learning for informative-frame selection in laryngoscopic videos through learned features
Nächster Artikel Combining DC-GAN with ResNet for blood cell image classification

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
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
2.
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
3.
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):979–998CrossRef
4.
5.
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
6.
Cortes C, Vapnik V (1995) Support-vector networks. Machine Learning 20(3):273–297
7.
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
8.
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):805–811CrossRef
9.
Efron B (1992) Bootstrap methods: another look at the jackknife. In: Breakthroughs in statistics. Springer, pp 569–593
10.
Fisher RA (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2):179–188CrossRef
11.
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):2201–2212CrossRef
12.
Hatipoglu N, Bilgin G (2017) Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med Biol Eng Comput 55(10):1829–1848CrossRef
13.
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
14.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: CVPR, vol 1 , p 3
15.
Kimura K, Takemoto T (1969) An endoscopic recognition of the atrophic border and its significance in chronic gastritis. Endoscopy 1(03):87–97CrossRef
16.
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):4793CrossRef
17.
LeCun Y, Bengio Y, Hinton G (2015) Deep Learning. Nature 521(7553):436CrossRef
18.
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 42:60–88CrossRef
19.
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
20.
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
21.
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 42:1–13CrossRef
22.
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):147–157CrossRef
23.
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):1285–1298CrossRef
24.
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
25.
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 77:9–15CrossRef
26.
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
27.
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
28.
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):1699–1713CrossRef
29.
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):1016–1022CrossRef
30.
Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2017) Mixup: Beyond empirical risk minimization. arXiv:1710.​09412
31.
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
32.
Zhou ZH, Li M (2005) Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17(11):1529–1541CrossRef
33.
Zhou ZH, Li M (2010) Semi-supervised learning by disagreement. Knowl Inf Syst 24(3):415–439CrossRef
Metadaten
Titel
Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training
verfasst von
Zongyao Li
Ren Togo
Takahiro Ogawa
Miki Haseyama
Publikationsdatum
27.03.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Medical & Biological Engineering & Computing / Ausgabe 6/2020
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-020-02159-z

Weitere Artikel der Ausgabe 6/2020

Medical & Biological Engineering & Computing 6/2020 Zur Ausgabe

Original Article

Dual-sectored transurethral ultrasound for thermal treatment of stress urinary incontinence: in silico studies in 3D anatomical models

Original Article

Stable gene selection by self-representation method in fuzzy sample classification

Original Article

Esophageal epithelium modeling based on globally coupled map: an approach toward precancerous lesion diagnosis

Review Article

The past, present, and prospective on UV–VIS–NIR skin photonics and spectroscopy—a wavelength guide

Original Article

Low-contrast X-ray enhancement using a fuzzy gamma reasoning model

Original Article

Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound