Deep learning for detecting diseases in gastrointestinal biopsy images

Aman Srivastava, Data Science Institute, University of Virginia
Saurav Sengupta, Data Science Institute, University of Virginia
Sung-Jun Kang, Data Science Institute, University of Virginia
Karan Kant, Data Science Institute, University of Virginia
Marium Khan, University of Virginia
Syed Asad Ali, Aga Khan University
Sean R. Moore, University of Virginia
Beatrice C. Amadi, Tropical Gastroenterology and Nutrition group, University of Zambia School of Medicine
Paul Kelly, Blizard Institute, Barts and The London School of Medicine, Queen Mary University of London
Sana Syed, University of Virginia
Donald E. Brown, Data Science Institute, University of Virginia

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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.