Looking in the Right Place for Anomalies: Explainable Ai Through Automatic Location Learning
| العنوان: | Looking in the Right Place for Anomalies: Explainable Ai Through Automatic Location Learning |
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| المؤلفون: | Joy T. Wu, Alexandros Karargyris, Arjun Sharma, Ken C. L. Wong, Yaniv Gur, Mehdi Moradi, Satyananda Kashyap, Tanveer Syeda-Mahmood |
| المصدر: | ISBI |
| بيانات النشر: | IEEE, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer science, Computer Vision and Pattern Recognition (cs.CV), Association (object-oriented programming), Computer Science - Computer Vision and Pattern Recognition, Inference, 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, Machine Learning (cs.LG), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Isolation (database systems), 0105 earth and related environmental sciences, business.industry, Anomaly (natural sciences), Deep learning, Artificial Intelligence (cs.AI), Recurrent neural network, Artificial intelligence, business, computer |
| الوصف: | Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses problems for their acceptance, particularly with clinicians. Current explainable AI methods offer justifications through visualizations such as heat maps but cannot guarantee that the network is focusing on the relevant image region fully containing the anomaly. In this paper, we develop an approach to explainable AI in which the anomaly is assured to be overlapping the expected location when present. This is made possible by automatically extracting location-specific labels from textual reports and learning the association of expected locations to labels using a hybrid combination of Bi-Directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM) and DenseNet-121. Use of this expected location to bias the subsequent attention-guided inference network based on ResNet101 results in the isolation of the anomaly at the expected location when present. The method is evaluated on a large chest X-ray dataset. Comment: 5 pages, Paper presented as a poster at the International Symposium on Biomedical Imaging, 2020, Paper Number 655 |
| DOI: | 10.1109/isbi45749.2020.9098370 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::782c9079a43177594aaa6cdf4a507acb https://doi.org/10.1109/isbi45749.2020.9098370 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....782c9079a43177594aaa6cdf4a507acb |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.1109/isbi45749.2020.9098370 |
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