Deep Learning for Detecting Diseases in Gastrointestinal Biopsy Images
| Autor: | Aman Srivastava, Sana Syed, Sung-Jun Kang, Donald E. Brown, Paul Kelly, Karan Kant, Beatrice Amadi, Saurav Sengupta, Sean R. Moore, Marium Mateen Khan, S. Asad Ali |
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| Rok vydání: | 2019 |
| Předmět: |
Medical diagnostic
medicine.diagnostic_test Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Convolutional neural network Visual recognition 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis Biopsy Medical imaging medicine 030211 gastroenterology & hepatology Artificial intelligence business Medical science Cluster analysis |
| Zdroj: | 2019 Systems and Information Engineering Design Symposium (SIEDS). |
| DOI: | 10.1109/sieds.2019.8735619 |
| Popis: | Machine learning and computer vision have found applications in medical science and, recently, pathology. In particular, deep learning methods for medical diagnostic imaging can reduce delays in diagnosis and give improved accuracy rates over other analysis techniques. This paper focuses on methods with applicability to automated diagnosis of images obtained from gastrointestinal biopsies. These deep learning techniques for biopsy images may help detect distinguishing features in tissues affected by enteropathies. Learning from different areas of an image, or looking for similar patterns in new images, allow for the development of potential classification or clustering models Techniques like these provide a cutting-edge solution to detecting anomalies. In this paper we explore state of the art deep learning architectures used for the visual recognition of natural images and assess their applicability in medical image analysis of digitized human gastrointestinal biopsy slides. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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