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Rajaraman S, Zamzmi G, Yang F, Xue Z, Antani SK. Data Characterization for Reliable AI in Medicine. RECENT TRENDS IN IMAGE PROCESSING AND PATTERN RECOGNITION : 5TH INTERNATIONAL CONFERENCE, RTIP2R 2022, KINGSVILLE, TX, USA, DECEMBER 01-02, 2022, REVISED SELECTED PAPERS. INTERNATIONAL CONFERENCE ON RECENT TRENDS IN IMAGE PROCESSING AND... 2023; 1704:3-11. [PMID: 36780238 PMCID: PMC9912175 DOI: 10.1007/978-3-031-23599-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Research in Artificial Intelligence (AI)-based medical computer vision algorithms bear promises to improve disease screening, diagnosis, and subsequently patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely Volume, Veracity, Validity, Variety, and Velocity, that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss each characteristic and the recent works conducted in our research lab that informed our understanding of the impact of these characteristics on the design of medical decision-making algorithms and outcome reliability.
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Affiliation(s)
- Sivaramakrishnan Rajaraman
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
| | - Ghada Zamzmi
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
| | - Feng Yang
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
| | - Zhiyun Xue
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
| | - Sameer K Antani
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
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Xue Z, Angara S, Guo P, Rajaraman S, Jeronimo J, Rodriguez AC, Alfaro K, Charoenkwan K, Mungo C, Domgue JF, Wentzensen N, Desai KT, Ajenifuja KO, Wikström E, Befano B, de Sanjosé S, Schiffman M, Antani S. Image Quality Classification for Automated Visual Evaluation of Cervical Precancer. MEDICAL IMAGE LEARNING WITH LIMITED AND NOISY DATA : FIRST INTERNATIONAL WORKSHOP, MILLAND 2022, HELD IN CONJUNCTION WITH MICCAI 2022, SINGAPORE, SEPTEMBER 22, 2022, PROCEEDINGS. MILLAND (WORKSHOP) (1ST : 2022 : SINGAPORE) 2022; 13559:206-217. [PMID: 36315110 PMCID: PMC9614805 DOI: 10.1007/978-3-031-16760-7_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Image quality control is a critical element in the process of data collection and cleaning. Both manual and automated analyses alike are adversely impacted by bad quality data. There are several factors that can degrade image quality and, correspondingly, there are many approaches to mitigate their negative impact. In this paper, we address image quality control toward our goal of improving the performance of automated visual evaluation (AVE) for cervical precancer screening. Specifically, we report efforts made toward classifying images into four quality categories ("unusable", "unsatisfactory", "limited", and "evaluable") and improving the quality classification performance by automatically identifying mislabeled and overly ambiguous images. The proposed new deep learning ensemble framework is an integration of several networks that consists of three main components: cervix detection, mislabel identification, and quality classification. We evaluated our method using a large dataset that comprises 87,420 images obtained from 14,183 patients through several cervical cancer studies conducted by different providers using different imaging devices in different geographic regions worldwide. The proposed ensemble approach achieved higher performance than the baseline approaches.
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Affiliation(s)
- Zhiyun Xue
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Sandeep Angara
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Peng Guo
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | | | - Jose Jeronimo
- National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | | | | | - Kittipat Charoenkwan
- Department of Obstetrics and Gynecology, Chiang Mai University, Chiang Mai, Thailand 50200
| | - Chemtai Mungo
- Department of Obstetrics and Gynecology, University of North Carolina-Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Joel Fokom Domgue
- Cameroon Baptist Convention Health Services, Bamenda, North West Region, Cameroon
- Department of Obstetrics and Gynecology, Faculty of Medicine and Biomedical Sciences, University of Yaoundé, Yaoundé, Cameroon
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nicolas Wentzensen
- National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Kanan T Desai
- National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | | | - Elisabeth Wikström
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Brian Befano
- Information Management Services, Calverton, MD, USA
| | - Silvia de Sanjosé
- National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Mark Schiffman
- National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Sameer Antani
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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