@inproceedings{4a9f22788b4a40d3884d4b92c07c86a0,
title = "Deep learning for detecting diseases in gastrointestinal biopsy images",
abstract = "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.",
keywords = "deep learning, disease detection, machine learning, medical imaging",
author = "Aman Srivastava and Saurav Sengupta and Kang, {Sung Jun} and Karan Kant and Marium Khan and Ali, {S. Asad} and Moore, {Sean R.} and Amadi, {Beatrice C.} and Paul Kelly and Sana Syed and Brown, {Donald E.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Systems and Information Engineering Design Symposium, SIEDS 2019 ; Conference date: 26-04-2019",
year = "2019",
month = apr,
doi = "10.1109/SIEDS.2019.8735619",
language = "English",
series = "2019 Systems and Information Engineering Design Symposium, SIEDS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 Systems and Information Engineering Design Symposium, SIEDS 2019",
address = "United States",
}