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Kalafi EY, Jodeiri A, Setarehdan SK, Lin NW, Rahmat K, Taib NA, Ganggayah MD, Dhillon SK. Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks. Diagnostics (Basel) 2021; 11:diagnostics11101859. [PMID: 34679557 PMCID: PMC8534785 DOI: 10.3390/diagnostics11101859] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 11/16/2022] Open
Abstract
The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer. The accuracy of the diagnosis is, however, highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. In this study, we propose a new framework for the classification of breast cancer lesions with an attention module in a modified VGG16 architecture. The adopted attention mechanism enhances the feature discrimination between the background and targeted lesions in ultrasound. We also propose a new ensembled loss function, which is a combination of binary cross-entropy and the logarithm of the hyperbolic cosine loss, to improve the model discrepancy between classified lesions and their labels. This combined loss function optimizes the network more quickly. The proposed model outperformed other modified VGG16 architectures, with an accuracy of 93%, and also, the results are competitive with those of other state-of-the-art frameworks for the classification of breast cancer lesions. Our experimental results show that the choice of loss function is highly important and plays a key role in breast lesion classification tasks. Additionally, by adding an attention block, we could improve the performance of the model.
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Affiliation(s)
- Elham Yousef Kalafi
- Data Science & Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia; (E.Y.K.); (M.D.G.)
| | - Ata Jodeiri
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, 1417935840 Tehran, Iran; (A.J.); (S.K.S.)
| | - Seyed Kamaledin Setarehdan
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, 1417935840 Tehran, Iran; (A.J.); (S.K.S.)
| | - Ng Wei Lin
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Kartini Rahmat
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
- Correspondence: (K.R.); (S.K.D.)
| | - Nur Aishah Taib
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mogana Darshini Ganggayah
- Data Science & Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia; (E.Y.K.); (M.D.G.)
| | - Sarinder Kaur Dhillon
- Data Science & Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia; (E.Y.K.); (M.D.G.)
- Correspondence: (K.R.); (S.K.D.)
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Fernandez-Rhodes L, Young KL, Lilly AG, Raffield LM, Highland HM, Wojcik GL, Agler C, M Love SA, Okello S, Petty LE, Graff M, Below JE, Divaris K, North KE. Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations. Circ Res 2020; 126:1816-1840. [PMID: 32496918 PMCID: PMC7285892 DOI: 10.1161/circresaha.120.315893] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Genome-wide association studies have revolutionized our understanding of the genetic underpinnings of cardiometabolic disease. Yet, the inadequate representation of individuals of diverse ancestral backgrounds in these studies may undercut their ultimate potential for both public health and precision medicine. The goal of this review is to describe the imperativeness of studying the populations who are most affected by cardiometabolic disease, to the aim of better understanding the genetic underpinnings of the disease. We support this premise by describing the current variation in the global burden of cardiometabolic disease and emphasize the importance of building a globally and ancestrally representative genetics evidence base for the identification of population-specific variants, fine-mapping, and polygenic risk score estimation. We discuss the important ethical, legal, and social implications of increasing ancestral diversity in genetic studies of cardiometabolic disease and the challenges that arise from the (1) lack of diversity in current reference populations and available analytic samples and the (2) unequal generation of health-associated genomic data and their prediction accuracies. Despite these challenges, we conclude that additional, unprecedented opportunities lie ahead for public health genomics and the realization of precision medicine, provided that the gap in diversity can be systematically addressed. Achieving this goal will require concerted efforts by social, academic, professional and regulatory stakeholders and communities, and these efforts must be based on principles of equity and social justice.
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Affiliation(s)
- Lindsay Fernandez-Rhodes
- Department of Biobehavioral Health, College of Health and Human Development, Pennsylvania State University, University Park, PA
| | - Kristin L Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Adam G Lilly
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Cary Agler
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Shelly-Ann M Love
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Samson Okello
- Department of Internal Medicine, Mbarara University of Science and Technology, Uganda
- University of Virginia, Charlottesville, VA
- Harvard TH Chan School of Public Health, Boston, MA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Vanderbilt, TN
- Department of Genetic Medicine, Vanderbilt University, Vanderbilt, TN
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Vanderbilt, TN
- Department of Genetic Medicine, Vanderbilt University, Vanderbilt, TN
| | - Kimon Divaris
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Carolina Center for Genome Sciences, Chapel Hill, NC
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Bentley AR, Callier SL, Rotimi CN. Evaluating the promise of inclusion of African ancestry populations in genomics. NPJ Genom Med 2020; 5:5. [PMID: 32140257 PMCID: PMC7042246 DOI: 10.1038/s41525-019-0111-x] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 12/16/2019] [Indexed: 12/24/2022] Open
Abstract
The lack of representation of diverse ancestral backgrounds in genomic research is well-known, and the resultant scientific and ethical limitations are becoming increasingly appreciated. The paucity of data on individuals with African ancestry is especially noteworthy as Africa is the birthplace of modern humans and harbors the greatest genetic diversity. It is expected that greater representation of those with African ancestry in genomic research will bring novel insights into human biology, and lead to improvements in clinical care and improved understanding of health disparities. Now that major efforts have been undertaken to address this failing, is there evidence of these anticipated advances? Here, we evaluate the promise of including diverse individuals in genomic research in the context of recent literature on individuals of African ancestry. In addition, we discuss progress and achievements on related technological challenges and diversity among scientists conducting genomic research.
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Affiliation(s)
- Amy R Bentley
- 1Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Shawneequa L Callier
- 1Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA.,2Department of Clinical Research and Leadership, The George Washington University School of Medicine and Health Sciences, Washington, DC USA
| | - Charles N Rotimi
- 1Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
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