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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Benign Strategy for Recommended Location Service Based on Trajectory Data Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Optimizing Breast Mass Segmentation Algorithms with Generative Adversarial Nets

verfasst von : Qi Yin, Haiwei Pan, Bin Yang, Xiaofei Bian, Chunling Chen

Erschienen in: Data Science

Verlag: Springer Singapore

Einloggen

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

search-config
loading …

Abstract

Breast cancer is the most ordinary malignant tumor in women worldwide. Early breast cancer screening is the key to reduce mortality. Clinical trials have shown that Computer Aided Design improves the accuracy of breast cancer detection. Segmentation of mammography is a critical step in Computer Aided Design. In recent years, FCN has been applied in the field of image segmentation. Generative Adversarial Networks is also popularized for its ability on generate images which is difficult to distinguish from real images, and have been applied in the image semantic segmentation domain. We apply the Dilated Convolutions to the partial convolutional layer of the Multi-FCN and use the ideas of Generative Adversarial Networks to train and correct our segmentation network. Experiments show that the Dice index of the model D-Multi-FCN-CRF-Adversarial Training on the datasets InBreast and DDSM-BCRP can be increased to 91.15% and 91.8%.

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
Vorheriges Kapitel Pedestrian Detection Method Based on SSD Model
Nächstes Kapitel Multiple Music Sentiment Classification Model Based on Convolutional Neural Network
Literatur
1.
Oeffinger, K.C.: Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA 314(15), 1599–1614 (2015)CrossRef
2.
Dalmiya, S.: Application of wavelet based k-means algorithm in mammogram segmentation. Int. J. Comput. Appl. 52(15), 15–19 (2016)
3.
Cordeiro, F.R.: An adaptive semi-supervised Fuzzy GrowCut algorithm to segment masses of regions of interest of mammographic images. Appl. Soft Comput. 46, 613–628 (2016)CrossRef
4.
5.
Cardoso, J.S.: Closed shortest path in the original coordinates with an application to breast cancer. Int. J. Pattern Recogn. Artif. Intell. 29(1), 1555002 (2015)CrossRef
6.
Greenspan, H.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)CrossRef
7.
Kallenberg, M.: Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016)CrossRef
8.
Zhu, W., Lou, Q., Vang, Y.S., Xie, X.: Deep multi-instance networks with sparse label assignment for whole mammogram classification. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 603–611. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-66179-7_​69CrossRef
9.
Dhungel, N., Carneiro, G.: Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms. In: IEEE International Symposium on Biomedical Imaging, pp. 760–763. IEEE (2015)
10.
Dhungel, N., Carneiro, G.: Deep structured learning for mass segmentation from mammograms. In: IEEE International Conference on Image Processing, pp. 2950–2954. IEEE, Quebec City (2015)
11.
12.
Zhu, W.: Adversarial deep structural networks for mammographic mass segmentation. CoRR (2016)
13.
Goodfellow, I.J., Pouget-Abadie, J.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems, pp. 2672–2680. MIT Press (2014)
14.
Radford, A.: Unsupervised representation learning with deep convolutional generative adversarial networks. Computer Science (2015)
15.
Wolterink, J.M.: Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans. Med. Imaging 36(12), 2536–2545 (2017)CrossRef
16.
Ganin, Y.: Domain-adversarial training of neural networks. Mach. Learn. Res. 17(1), 2096-2030 (2017)MathSciNet
17.
18.
Luc, P.: Semantic segmentation using adversarial networks. CoRR (2016)
19.
Kohl, S.: Adversarial networks for the detection of aggressive prostate cancer. CoRR (2017)
20.
Dai, W.: Scan: structure correcting adversarial network for chest x-rays organ segmentation. CoRR (2017)
21.
22.
23.
Yu, F.: Multi-scale context aggregation by dilated convolutions (2015)
24.
Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease. In: Zuluaga, M.A., Bhatia, K., Kainz, B., Moghari, M.H., Pace, D.F. (eds.) RAMBO/HVSMR-2016. LNCS, vol. 10129, pp. 95–102. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-52280-7_​9CrossRef
25.
Chen, L.C.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. Comput. Sci. 4, 357–361 (2014)
26.
Chen, L.C.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2016)CrossRef
27.
Chen, L.C.: Rethinking atrous convolution for semantic image segmentation. CoRR (2017)
28.
Chen, L.C.: Encoder-decoder with atrous separable convolution for semantic image segmentation. CoRR (2018)
29.
Zhu, W.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: AAAI, vol. 2 (2016)
30.
Zhu, W., Miao, J.: Hierarchical extreme learning machine for unsupervised representation learning. In: International Joint Conference on Neural Networks, pp. 1–8. IEEE (2015)
31.
Long, J.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2014)
32.
Zeiler, M.D., Krishnan, D.: Deconvolutional networks. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2528–2535. IEEE (2010)
33.
Zheng, S.: Conditional Random Fields as Recurrent Neural Networks. CoRR, pp. 1529–1537 (2015)
34.
Chen, L C.: Attention to scale: scale-aware semantic image segmentation. CoRR, pp. 3640–3649 (2015)
35.
Sermanet, P.: OverFeat: integrated recognition, localization and detection using convolutional networks. Eprint Arxiv (2013)
36.
Ines, C.: INbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)CrossRef
37.
Ball, J.E., Bruce, LM.: Digital mammographic computer aided diagnosis (CAD) using adaptive level set segmentation. In: International Conference of the IEEE Engineering in Medicine Biology Society. IEEE (2007)
38.
Kingma, D.: Adam: a method for stochastic optimization. Computer Science (2014)
39.
Szegedy, C.: Intriguing properties of neural networks. Computer Science (2013)
40.
Metadaten
Titel
Optimizing Breast Mass Segmentation Algorithms with Generative Adversarial Nets
verfasst von
Qi Yin
Haiwei Pan
Bin Yang
Xiaofei Bian
Chunling Chen
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Data Science

Print ISBN: 978-981-15-0117-3

Electronic ISBN: 978-981-15-0118-0

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-15-0118-0_47

Premium Partner

    Bildnachweise