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:
Einleitung „Künstliche Intelligenz im Gesundheitswesen“ Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

35. Erklärbare KI in der medizinischen Diagnose – Erfolge und Herausforderungen

verfasst von : Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel, Sheraz Ahmed

Erschienen in: Künstliche Intelligenz im Gesundheitswesen

Verlag: Springer Fachmedien Wiesbaden

Einloggen

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

search-config
loading …

Zusammenfassung

Der große Erfolg moderner, bildbasierter KI-Methoden und das damit einhergehende Interesse für die Anwendung von KI in kritischen Entscheidungsprozessen führte zu einem Anstieg der Bemühungen, intelligente Systeme transparent und erklärbar zu gestalten. Besonders im medizinischen Kontext, wo computergestützte Entscheidungen direkten Einfluss auf die Behandlung und das Wohlsein von Patienten haben können, ist Transparenz für den sicheren Übergang von Forschung in die Praxis von höchster Wichtigkeit. Dieser Beitrag beschäftigt sich mit dem aktuellen Stand moderner Methoden zur Erklärung und Interpretation von Deep-Learning-basierten KI-Algorithmen in Anwendungen der medizinischen Forschung und Diagnose von Krankheiten. Zunächst werden erste bemerkenswerte Erfolge im Einsatz erklärbarer KI zur Validierung bekannter und Exploration potenzieller Biomarker sowie Methoden zur nachträglichen Korrektur von KI-Modellen aufgezeigt. Im Anschluss werden einige verbleibende Herausforderungen, die der Anwendung von KI als klinische Entscheidungshilfe im Weg stehen, kritisch diskutiert und Empfehlungen für die Ausrichtung zukünftiger Forschung ausgesprochen.

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 Abrechnung medizinischer Leistungen mit künstlicher Intelligenz
Nächstes Kapitel Explainable AI im Gesundheitswesen
Literatur
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digital Medicine, 1(1), 1–8.CrossRef
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., & Kim, B. (2018). Sanity checks for saliency maps. Advances in Neural Information Processing Systems, 31, 9505–9515.
Alipour, K., Schulze, J. P., Yao, Y., Ziskind, A., & Burachas, G. (2020). A study on multimodal and interactive explanations for visual question answering. arXiv preprint arXiv:​2003.​00431.
Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018). Advanced machine learning in action: Identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digital Medicine, 1(1), 1–7.CrossRef
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., & Chatila, R. (2020) Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
Ba, J., Mnih, V., & Kavukcuoglu, K. (2014). Multiple object recognition with visual attention. arXiv preprint arXiv:​1412.​7755.
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K. R., & Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7), e0130140.
Bau, D., Zhou, B., Khosla, A., Oliva, A., & Torralba A. (2017). Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, Institute of Electrical and Electronics Engineers (S. 6541–6549), 21.07.–26.07.2017, Honolulu, The Computer Vision Foundation (CVF).
Beam, A. L., & Kohane, I. S. (2016). Translating artificial intelligence into clinical care. JAMA, 316(22), 2368–2369.CrossRef
Beede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P., & Vardoulakis, L. M. (2020). A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In Proceedings of the 2020 CHI conference on human factors in computing systems (S. 1–12), 25.04.–30.04.2020, Honolulu, Special Interest Group on Computer-Human Interaction (SIGCHI).
Buchanan, B., Sutherland, G., & Feigenbaum, E. A. (1969). Heuristic DENDRAL: A program for generating explanatory hypotheses in organic chemistry. In B. Meltzer & D. Michie (Hrsg.), Machine intelligence (Bd. 4, S. 209–254). Edinburgh University Press.
Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517–518.CrossRef
Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., & Stumpe, M. C., & Terry, M. (2019a). Human-centered tools for coping with imperfect algorithms during medical decision-making. In Proceedings of the 2019 CHI conference on human factors in computing systems (S. 1–14), 04.05.–09.05.2019, Glasgow, Special Interest Group on Computer-Human Interaction (SIGCHI).
Cai, C. J., Jongejan, J., & Holbrook, J. (2019b). The effects of example-based explanations in a machine learning interface. In Proceedings of the 24th international conference on intelligent user interfaces (S. 258–262), 16.03.–20.03.2019, Los Angeles, Special Interest Group on Computer-Human Interaction (SIGCHI).
Carrieri A.P., Haiminen N., Maudsley-Barton S., Gardiner L.J., Murphy B., Mayes A., Paterson S., Grimshaw S., Winn M., Shand C., & Rowe, W. (2020). Explainable AI reveals key changes in skin microbiome associated with menopause, smoking, aging and skin hydration. bioRxiv.
Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.CrossRef
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, Association for Computing Machinery (ACM), Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) (S. 1721–1730), 10.08.–13.08.2015, Sydney, SIGKDD,.
Cole, E. B., Zhang, Z., Marques, H. S., Hendrick, R. E., Yaffe, M. J., & Pisano, E. D. (2014). Impact of computer-aided detection systems on radiologist accuracy with digital mammography. American Journal of Roentgenology, 203(4), 909–916.CrossRef
Coppola, D., Kuan Lee, H., & Guan, C. (2020). Interpreting mechanisms of prediction for skin cancer diagnosis using multi-task learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, Institute of Electrical and Electronics Engineers, The Computer Vision Foundation (IEEE, CVF), virtuelle Konferenz (S. 734–735), 14.06.–19.06.2020, CVF.
Couteaux, V., Nempont, O., Pizaine, G., & Bloch, I. (2019). Towards interpretability of segmentation networks by analyzing DeepDreams. Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support, 11797, 56–63.
Cruz-Roa, A. A., Ovalle, J. E. A., Madabhushi A., & Osorio, F. A. G. (2013). A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In International conference on medical image computing and computer-assisted intervention, (S. 403–410), 22.09.–26.09.2013, Nagoya, The Medical Image Computing and Computer Assisted Intervention Society (MICCAI). Springer.
DFKI. (2020a). exAID – Bringing the power of deep learning to clinical practice! Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI, Hrsg.). https://​exaid.​kl.​dfki.​de/​. Zugegriffen: 13. Okt. 2020.
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., Fei-Fei, L. (2009) Imagenet: A large-scale hierarchical image database. In IEEE conference on computer vision and pattern recognition, Institute of Electrical and Electronics Engineers (IEEE) (S. 248–255), 20.06–25.06.2009, Miami, The Computer Vision Foundation (CVF).
Eitel, F., & Ritter, K. (2019). Testing the Robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer’s disease classification. Alzheimer’s Disease Neuroimaging Initiative (ADNI, Hrsg.). Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support, 11797(1), 3–11.
Elwyn, G., Scholl, I., Tietbohl, C., Mann, M., Edwards, A. G., Clay, C., Légaré, F., Van der Weijden, T., Lewis, C. L., Wexler, R. M., & Frosch, D. L. (2013). “Many miles to go”: A systematic review of the implementation of patient decision support interventions into routine clinical practice. BMC Medical Informatics and Decision Making, 13(2), 1–10.
Erion, G., Janizek, J. D., Sturmfels, P., Lundberg, S., & Lee, S. I. (2019). Learning explainable models using attribution priors. arXiv preprint arXiv:​1906.​10670.
Essemlali, A., St-Onge, E., Descoteaux, M., & Jodoin, P. M. (2020). Understanding Alzheimer disease’s structural connectivity through explainable AI. Medical Imaging with Deep Learning, 121, 217–229 (PMLR).
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter S. M., Blau, H. M., & Thrun, S. (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
Fong, R., Patrick, M., & Vedaldi, A. (2019). Understanding deep networks via extremal perturbations and smooth masks. In Proceedings of the IEEE International conference on computer vision, Institute of Electrical and Electronics Engineers (IEEE) (S. 2950–2958), 27.10.–02.11.2019, Seoul, The Computer Vision Foundation (CVF).
Ghorbani, A., Wexler, J., Zou, J. Y., & Kim, B. (2019). Towards automatic concept-based explanations. Advances in Neural Information Processing Systems, 32, 9277–9286.
Ghosh, S., Elenius, D., Li, W., Lincoln, P., Shankar, N., & Steiner, W. (2016). ARSENAL: Automatic requirements specification extraction from natural language. In NASA Formal Methods Symposium (S. 41–46), 07.06.–09.06.2016, Minneapolis, National Aeronautics and Space Administration (NASA). Springer.
Graziani, M., Andrearczyk, V., & Müller, H. (2019) Visualizing and interpreting feature reuse of pretrained CNNs for histopathology. In MVIP 2019: Irish machine vision and image processing conference proceedings, irish pattern recognition and classification society, 28.08.–30.08.2019, Dublin, Technological University Dublin.
Graziani, M., Andrearczyk, V., & Müller, H. (2018). Regression concept vectors for bidirectional explanations in histopathology. Understanding and Interpreting Machine Learning in Medical Image Computing Applications, 11038, 124–132.
Graziani, M., Otálora, S., Muller, H., & Andrearczyk V. (2020). Guiding CNNs towards relevant concepts by multi-task and adversarial learning. arXiv preprint arXiv:​2008.​01478.
Guan, J. (2019). Artificial intelligence in healthcare and medicine: Promises, ethical challenges and governance. Chinese Medical Sciences Journal, 34(2), 76–83.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., & Kim, R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410.CrossRef
Hendricks, L. A., Hu, R., Darrell, T., & Akata, Z. (2018). Grounding visual explanations. In Proceedings of the European Conference on Computer Vision (ECCV) (S. 264–279), 08.09.–14.09.2018, München, The Computer Vision Foundation (CVF).
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016). Generating visual explanations. In European conference on computer vision (S. 3–19), 08.10.–16.10.2016, Amsterdam, The Computer Vision Foundation (CVF). Springer.
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82–97.CrossRef
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312.
Holzinger, A., Biemann, C., Pattichis, C. S., Kell, D. B. (2017). What do we need to build explainable AI systems for the medical domain? arXiv preprint arXiv:​1712.​09923.
Hooker, S., Erhan, D., Kindermans, P. J., & Kim, B. (2019). A benchmark for interpretability methods in deep neural networks. Advances in Neural Information Processing Systems, 32, 9737–9748.
Huk Park, D., Hendricks, L. A., Akata, Z., Rohrbach, A., Schiele, B., Darrell, T., & Rohrbach, M. (2018) Multimodal explanations: Justifying decisions and pointing to the evidence. In Proceedings of the IEEE conference on computer vision and pattern recognition, Institute of Electrical and Electronics Engineering (IEEE) (S. 8779–8788), 19.06.–21.06.2018, Salt Lake City, The Computer Vision Foundation (CVF).
Izadyyazdanabadi, M., Belykh, E., Cavallo, C., Zhao, X., Gandhi, S., Moreira, L. B., Eschbacher, J., Nakaji, P., Preul, M. C., & Yang, Y. (2018). Weakly-supervised learning-based feature localization for confocal laser endomicroscopy glioma images. In International conference on medical image computing and computer-assisted intervention (S. 300–308), 16.09.–20.09.2019, Granada, The Medical Image Computing and Computer Assisted Intervention Society (MICCAI). Springer.
Jaderberg, M., Simonyan, K., & Zisserman, A. (2015). Spatial transformer networks. Advances in Neural Information Processing Systems, 28, 2017–2025.
Jansen, C., Penzel, T., Hodel, S., Breuer, S., Spott, M., & Krefting, D. (2019). Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(12), 123129.
Jirotka, M., Procter, R., Hartswood, M., Slack, R., Simpson, A., Coopmans, C., Hinds, C., & Voss, A. (2005). Collaboration and trust in healthcare innovation: The eDiaMoND case study. Computer Supported Cooperative Work (CSCW), 14(4), 369–398.CrossRef
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach H., & Wortman Vaughan, J. (2020). Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (S. 1–14), 25.04.–30.04.2020, Honolulu, Special Interest Group on Computer-Human Interaction (SIGCHI).
Kawahara, J., Daneshvar, S., Argenziano, G., & Hamarneh, G. (2018). Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE Journal of Biomedical and Health Informatics, Institute of Electrical and Electronics Engineers (IEEE), 23(2), 538–546.
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler J., & Viegas F. (2018). Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International Conference on Machine Learning, 80, 2668–2677.
Kohli, A., & Jha, S. (2018). Why CAD failed in mammography. Journal of the American College of Radiology, 15(3), 535–537.CrossRef
Lakkaraju, H., Kamar, E., Caruana, R., & Leskovec, J., (2019) Faithful and customizable explanations of black box models. In Proceedings of the 2019 AAAI/ACM conference on AI, Ethics, and Society, Association for the Advancement of Artificial Intelligence, Association for Computing Machinery (AAAI, ACM) (S. 131–138), 27.01.–01.02.2019, Honolulu, AAAI.
Lucieri, A., Bajwa, M. N., Dengel, A., & Ahmed, S. (2020b). Explaining ai-based decision support systems using concept localization maps. arXiv preprint arXiv:​2005.​01399.
Lucieri, A., Bajwa, M. N., Braun, S. A., Malik, M. I., Dengel, A., & Ahmed, S. (2020a). On interpretability of deep learning based skin lesion classifiers using concept activation vectors. In International Joint Conference on Neural Networks (IJCNN) (S. 1–10), 19.07.–24.07.2020, Glasgow, Computational Intelligence Society (CIS).
Lundberg, S. M., & Lee ,S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Mahendran, A., & Vedaldi, A. (2016). Visualizing deep convolutional neural networks using natural pre-images. International Journal of Computer Vision, 120(3), 233–255.CrossRef
Mitsuhara, M., Fukui, H., Sakashita, Y., Ogata, T., Hirakawa, T., Yamashita, T., & Fujiyoshi, H. (2019). Embedding human knowledge into deep neural network via attention map. arXiv preprint arXiv:​1905.​03540.
Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1–15.
Munir, M., Siddiqui, S. A., Küsters, F., Mercier, D., Dengel, A., & Ahmed, S. (2019). TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features. In International conference on artificial neural networks (S. 426–439), 17.09.–19.09.2019, München, European Neural Network Society (ENNS). Springer.
Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvintsev, A. (2018). The building blocks of interpretability. Distill, 3(3), e10.
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books.
Rabold, J., Deininger, H., Siebers, M., & Schmid, U. (2019). Enriching visual with verbal explanations for relational concepts–combining LIME with Aleph. In Joint European conference on machine learning and knowledge discovery in databases (S. 180–192), 16.09.–20.09.2019, Würzburg, Julius-Maximilians-Universität Würzburg. Springer.
Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., & Patel, B. N. (2018) Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS medicine, 15(11), e1002686.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, Association for Computing Machinery, Special Interest Group on Knowledge Discovery and Data Mining (ACM, SIGKDD) (S. 1135–1144), 13.08.–17.08.2016, San Francisco, SIGKDD.
Rieger, L., Singh, C., Murdoch, W. J., & Yu, B. (2019). Interpretations are useful: Penalizing explanations to align neural networks with prior knowledge. arXiv preprint arXiv:​1909.​13584.
Ross, A. S., Hughes, M. C., & Doshi-Velez, F. (2017). Right for the right reasons: Training differentiable models by constraining their explanations. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (S. 2662–2670), 19.08.–25.08.2017, Melbourne, International Joint Conference on Artificial Intelligence (IJCAI).
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.CrossRef
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Müller, K. R. (2016). Evaluating the visualization of what a deep neural network has learned. IEEE Transactions on Neural Networks and Learning Systems, 28(11), 2660–2673.CrossRef
Sayres, R., Taly, A., Rahimy, E., Blumer, K., Coz, D., Hammel, N., Krause, J., Narayanaswamy, A., Rastegar, Z., Wu, D., Xu, S., Barb, S., Joseph, A., Shumski, M., Smith, J., Sood, A. B., Corrado, G. S., Peng, L., & Webster, D. R. (2019). Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology, 126(4), 552–564.CrossRef
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., & Batra, D. (2016). Grad-CAM: Why did you say that? arXiv preprint arXiv:​1611.​07450.
Shortliffe, E. H. (1974). MYCIN: A rule-based computer program for advising physicians regarding antimicrobial therapy selection. In Proceedings of the 1974 Annual ACM conference – Volume 2, Association for Computing Machinery (ACM) (S. 2950–2958), San Diego, ACM.
Shrikumar, A., Greenside, P., Shcherbina, A., & Kundaje, A. (2016). Not just a black box: Learning important features through propagating activation differences. arXiv preprint arXiv:​1605.​01713.
Shrikumar, A., Greenside, P., & Kundaje, A. (2017). Learning important features through propagating activation differences. In International conference on machine learning, (S. 3145–3153), 06.08.–11.08.2017, Sydney, The International Machine Learning Society (IMLS), .
Sikka, K., Silberfarb, A., Byrnes, J., Sur, I., Chow, E., Divakaran, A., & Rohwer, R. (2020). Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks. arXiv preprint arXiv:​2003.​07344.
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:​1312.​6034.
Singh, A., Sengupta, S., & Lakshminarayanan, V. (2020). Explainable deep learning models in medical image analysis. arXiv preprint arXiv:​2005.​13799.
Sonntag, D., Nunnari, F., & Profitlich, H. J. (2020). The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical report. arXiv preprint arXiv:​2005.​09448.
Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020) Interpretability of machine learning based prediction models in healthcare. arXiv preprint arXiv:​2002.​08596.
Teach, R. L., & Shortliffe, E. H. (1981). An analysis of physician attitudes regarding computer-based clinical consultation systems. Computers and Biomedical Research, 14(6), 542–558.CrossRef
Tjoa, E., & Guan, C. (2019). A survey on explainable artificial intelligence (XAI): Towards medical XAI. arXiv preprint arXiv:​1907.​07374.
Tjoa, E., & Guan, C. (2020). Quantifying explainability of saliency methods in deep neural networks. arXiv preprint arXiv:​2009.​02899.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
Yamamoto, Y., Tsuzuki, T., Akatsuka, J., Ueki, M., Morikawa, H., Numata, Y., Takahara, T., Tsuyuki, T., Tsutsumi, K., Nakazawa, R., & Shimizu, A. (2019). Automated acquisition of explainable knowledge from unannotated histopathology images. Nature Communications, 10(1), 1–9.CrossRef
Yan, Y., Kawahara, J., Hamarneh, G. (2019). Melanoma recognition via visual attention. In International Conference on Information Processing in Medical Imaging (S. 793–804), 02.06.–07.06.2019, Hong Kong, The Hong Kong University of Science and Technology (HKUST). Springer.
Yang, Q., Steinfeld, A., & Zimmerman, J. (2019b) Unremarkable ai: Fitting intelligent decision support into critical, clinical decision-making processes. In Proceedings of the 2019 CHI conference on human factors in computing systems (S. 1–11), 04.05.–09.05.2019, Glasgow, Special Interest Group on Computer-Human Interaction (SIGCHI).
Yang, H. L., Kim, J. J., Kim, J. H., Kang, Y. K., Park, D. H., Park, H. S., Kim, H. K., & Kim, M. S. (2019a). Weakly supervised lesion localization for age-related macular degeneration detection using optical coherence tomography images. PloS one, 14(4), e0215076.
Zeiler, M. D., & Fergus, R. (2014) Visualizing and understanding convolutional networks. In European Conference on Computer Vision (ECCV), (S. 818–833), 06.09.–12.09.2014, Zürich, The Computer Vision Foundation (CVF). Springer.
Zhang, R., Tan, S., Wang, R., Manivannan, S., Chen, J., Lin, H., & Zheng, W. S. (2019). Biomarker localization by combining CNN classifier and generative adversarial network. In International conference on medical image computing and computer-assisted intervention (S. 209–217), 13.10.–17.10.2019, Shenzhen, The Medical Image Computing and Computer Assisted Intervention Society (MICCAI). Springer.
Zhang Z., Xie Y., Xing F., McGough M., Yang L. (2017) MDNet: A semantically and visually interpretable medical image diagnosis network. In Proceedings of the IEEE conference on computer vision and pattern recognition, Institute of Electrical and Electronics Engineers (IEEE) (S. 6428–6436), 21.07.–26.07.2017, Honolulu, The Computer Vision Foundation (CVF).
Zhou, B., Sun, Y., Bau, D., & Torralba A. (2018). Interpretable basis decomposition for visual explanation. In Proceedings of the European Conference on Computer Vision (ECCV) (S. 119–134), 08.09.–14.09.2018, München, The Computer Vision Foundation (CVF).
Metadaten
Titel
Erklärbare KI in der medizinischen Diagnose – Erfolge und Herausforderungen
verfasst von
Adriano Lucieri
Muhammad Naseer Bajwa
Andreas Dengel
Sheraz Ahmed
Copyright-Jahr
2022
Buch
Künstliche Intelligenz im Gesundheitswesen

Print ISBN: 978-3-658-33596-0

Electronic ISBN: 978-3-658-33597-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-658-33597-7_35

Premium Partner

    Bildnachweise