nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems

verfasst von : Changhee Han, Leonardo Rundo, Kohei Murao, Takafumi Nemoto, Hideki Nakayama

Erschienen in: Artificial Intelligence Applications and Innovations

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs’ clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.

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 Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment

Nächstes Kapitel Know Yourself: An Adaptive Causal Network Model for Therapeutic Intervention for Regaining Cognitive Control

Hwang, E.J., Park, S., Jin, K., et al.: Development and validation of a deep learning-based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs. Clin. Infect. Dis. 69(5), 739–747 (2018)CrossRef

Wu, N., Phang, J., Park, J., et al.: Deep neural networks improve radiologists’ performance in breast cancer screening. In: Proceedings of the International Conference on Medical Imaging with Deep Learning (MIDL). arXiv:1907.08612 (2019)

McKinney, S.M., Sieniek, M., Godbole, V., et al.: International evaluation of an AI system for breast cancer screening. Nature 577(7788), 89–94 (2020)CrossRef

Allen Jr., B., Seltzer, S.E., Langlotz, C.P., et al.: A road map for translational research on artificial intelligence in medical imaging: from the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop. J. Am. Coll. Radiol. 16(9, Part A), 1179–1189 (2019)CrossRef

Clark, K., Vendt, B., Smith, K., et al.: The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26, 1045–1057 (2013). https://doi.org/10.1007/s10278-013-9622-7CrossRef

Tokunaga, H., Teramoto, Y., Yoshizawa, A., Bise, R.: Adaptive weighting multi-field-of-view CNN for semantic segmentation in pathology. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12597–12606 (2019)

Kanayama, T., et al.: Gastric cancer detection from endoscopic images using synthesis by GAN. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 530–538. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32254-0_59CrossRef

Han, C., Hayashi, H., Rundo, L., et al.: GAN-based synthetic brain MR image generation. In: Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), pp. 734–738 (2018)

Han, C., et al.: Infinite brain MR images: PGGAN-based data augmentation for tumor detection. In: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (eds.) Neural Approaches to Dynamics of Signal Exchanges. SIST, vol. 151, pp. 291–303. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-8950-4_27CrossRef

10.

Han, C., Rundo, L., Araki, R., et al.: Combining noise-to-image and image-to-image GANs: brain MR image augmentation for tumor detection. IEEE Access 7, 156966–156977 (2019)CrossRef

11.

Han, C., Murao, K., Noguchi, T., et al.: Learning more with less: conditional PGGAN-based data augmentation for brain metastases detection using highly-rough annotation on MR images. In: Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), pp. 119–127 (2019)

12.

Han, C., Kitamura, Y., Kudo, A., et al.: Synthesizing diverse lung nodules wherever massively: 3D multi-conditional GAN-based CT image augmentation for object detection. In: Proceedings of the International Conference on 3D Vision (3DV), pp. 729–737 (2019)

13.

Han, C., Murao, K., Satoh, S., Nakayama, H.: Learning more with less: GAN-based medical image augmentation. Med. Imaging Tech. 37(3), 137–142 (2019)

14.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 2672–2680 (2014)

15.

Hsieh, J.: Computed Tomography: Principles, Design, Artifacts, and Recent Advances. SPIE, Bellingham (2009)

16.

Brown, R.W., Cheng, Y.N., Haacke, E.M., et al.: Magnetic Resonance Imaging: Physical Principles and Sequence Design. Wiley, Hoboken (2014)CrossRef

17.

Tmenova, O., Martin, R., Duong, L.: CycleGAN for style transfer in X-ray angiography. Int. J. Comput. Assist. Radiol. Surg. 14(10), 1785–1794 (2019). https://doi.org/10.1007/s11548-019-02022-zCrossRef

18.

Frid-Adar, M., Diamant, I., Klang, E., et al.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321–331 (2018)CrossRef

19.

Madani, A., Moradi, M., Karargyris, A., Syeda-Mahmood, T.: Chest X-ray generation and data augmentation for cardiovascular abnormality classification. In: Proceedings of the Medical Imaging: Image Processing, vol. 10574, 105741M (2018)

20.

Konidaris, F., Tagaris, T., Sdraka, M., Stafylopatis, A.: Generative adversarial networks as an advanced data augmentation technique for MRI data. In: Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), pp. 48–59 (2019)

21.

Finlayson, S.G., Lee, H., Kohane, I.S., Oakden-Rayner, L.: Towards generative adversarial networks as a new paradigm for radiology education. In: Proceedings of the Machine Learning for Health (ML4H) Workshop. arXiv:1812.01547 (2018)

22.

Rundo, L., Militello, C., Russo, G., Vitabile, S., Gilardi, M.C., Mauri, G.: GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model. Nat. Comput. 17(3), 521–536 (2017). https://doi.org/10.1007/s11047-017-9636-zCrossRef

23.

Stinis, P., Hagge, T., Tartakovsky, A.M., Yeung, E.: Enforcing constraints for interpolation and extrapolation in generative adversarial networks. J. Comput. Phys. 397, 108844 (2019)CrossRef

24.

Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: Proceedings of the International Conference on Learning Representations (ICLR). arXiv:1710.10196v3 (2018)

25.

Al-Shabi, M., Lan, B.L., Chan, W.Y., et al.: Lung nodule classification using deep local-global networks. Int. J. Comput. Assist. Radiol. Surg. 14(10), 1815–1819 (2019). https://doi.org/10.1007/s11548-019-01981-7CrossRef

26.

Honda, T., Matsubara, Y., Neyama, R., et al.: Multi-aspect mining of complex sensor sequences. In: Proceedings of the IEEE International Conference on Data Mining (ICDM) (2019, in press)

27.

Allen, I.E., Seaman, C.A.: Likert scales and data analyses. Qual. Prog. 40(7), 64–65 (2007)

28.

Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on Explainable Artificial Intelligence (XAI). IEEE Access 6, 52138–52160 (2018)CrossRef

29.

Abràmoff, M.D., Lavin, P.T., Birch, M., et al.: Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit. Med. 1(1), 39 (2018)CrossRef

30.

Nair, T., Precup, D., Arnold, D.L., Arbel, T.: Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Med. Image Anal. 59, 101557 (2020)CrossRef

31.

Jankharia, G.R.: Commentary-radiology in India: the next decade. Indian J. Radiol. Imaging 18(3), 189 (2008)CrossRef

32.

O’Connor, D., Potler, N.V., Kovacs, M., et al.: The healthy brain network serial scanning initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions. Gigascience 6(2), giw011 (2017)CrossRef

33.

Rundo, L., Han, C., Nagano, Y., et al.: USE-Net: incorporating squeeze-and-excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 365, 31–43 (2019)CrossRef

34.

Vandenberghe, M.E., Scott, M.L.J., Scorer, P.W., et al.: Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci. Rep. 7, 45938 (2017)CrossRef

35.

Li, X., Wang, Y., Li, D.: Medical data stream distribution pattern association rule mining algorithm based on density estimation. IEEE Access 7, 141319–141329 (2019)CrossRef

36.

Agn, M., Law, I., af Rosenschöld, P.M., Van Leemput, K.: A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients. In: Proceedings of the Medical Imaging: Image Processing, vol. 9784, p. 97841D (2016)

37.

Abi-Aad, K.R., Anderies, B.J., Welz, M.E., Bendok, B.R.: Machine learning as a potential solution for shift during stereotactic brain surgery. Neurosurgery 82(5), E102–E103 (2018)CrossRef

38.

Yang, Q., Yan, P., Zhang, Y., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018)CrossRef

39.

Rumbold, J.M.M., Pierscionek, B.: The effect of the general data protection regulation on medical research. J. Med. Internet Res. 19(2), e47 (2017)CrossRef

40.

Sobin, L.H., Gospodarowicz, M.K., Wittekind, C.: TNM Classification of Malignant Tumours, 7th edn. Wiley, Hoboken (2011)

41.

Nawata, K., Matsumoto, A., Kajihara, R., Kimura, M.: Evaluation of the distribution and factors affecting blood pressure using medical checkup data in Japan. Health 9(1), 124–137 (2016)CrossRef

42.

Mansour, R.P.: Visual charting method for creating electronic medical documents. US Patent 10,262,106, 16 April 2019

43.

Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516–524. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_59CrossRef

44.

Chen, A., Zhang, Z., Li, Q., et al.: Feasibility study for implementation of the AI-powered Internet+ Primary Care Model (AiPCM) across hospitals and clinics in Gongcheng county, Guangxi, China. Lancet 394, S44 (2019)CrossRef

45.

Laplante-Lévesque, A., Abrams, H., Bülow, M., et al.: Hearing device manufacturers call for interoperability and standardization of internet and audiology. Am. J. Audiol. 25(3S), 260–263 (2016)CrossRef

46.

Morley, J., Taddeo, M., Floridi, L.: Google Health and the NHS: overcoming the trust deficit. Lancet Digit. Health 1(8), e389 (2019)CrossRef

47.

Rossini, G., Parrini, S., Castroflorio, T., et al.: Diagnostic accuracy and measurement sensitivity of digital models for orthodontic purposes: a systematic review. Am. J. Orthod. Dentofacial Orthop. 149(2), 161–170 (2016)CrossRef

48.

Huang, K., Cheng, H., Zhang, Y., et al.: Medical knowledge constrained semantic breast ultrasound image segmentation. In: Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 1193–1198 (2018)

49.

Krittanawong, C.: The rise of artificial intelligence and the uncertain future for physicians. Eur. J. Intern. Med. 48, e13–e14 (2018)CrossRef

50.

Li, H., Jiang, G., Zhang, J., et al.: Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images. NeuroImage 183, 650–665 (2018)CrossRef

51.

Jain, A., Ratnoo, S., Kumar, D.: Addressing class imbalance problem in medical diagnosis: a genetic algorithm approach. In: Proceedings of the International Conference on Information, Communication, Instrumentation and Control (ICICIC), pp. 1–8 (2017)

52.

Wartman, S.A., Combs, C.D.: Reimagining medical education in the age of AI. AMA J. Ethics 21(2), 146–152 (2019)CrossRef

53.

Lin, C.H., Chang, C., Chen, Y., et al.: COCO-GAN: generation by parts via conditional coordinating. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 4512–4521 (2019)

54.

Xu, Z., Wang, X., Shin, H., et al.: Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network. arXiv:1907.03728 (2019)

Titel: Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems
verfasst von: Changhee Han
Leonardo Rundo
Kohei Murao
Takafumi Nemoto
Hideki Nakayama
Verlag: Springer International Publishing
Buch: Artificial Intelligence Applications and Innovations
Print ISBN: 978-3-030-49185-7

Electronic ISBN: 978-3-030-49186-4

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-49186-4_27

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner