Access the full text.
Sign up today, get DeepDyve free for 14 days.
J. Krutzinna, L. Floridi (2019)
The Ethics of Medical Data DonationPhilosophical Studies Series
P. Carter, G. Laurie, M. Dixon-Woods (2015)
The social licence for research: why care.data ran into troubleJournal of Medical Ethics, 41
T. Kalaiselvi, S. Padmapriya, Karuppanagounder Somasundaram, S. Praveenkumar (2022)
E-Tanh: a novel activation function for image processing neural network modelsNeural Computing and Applications, 34
Bas Velden, H. Kuijf, K. Gilhuijs, M. Viergever (2021)
Explainable artificial intelligence (XAI) in deep learning-based medical image analysisMedical image analysis, 79
(2021)
Novel artificial intelligence learning models for COVID-19 detection from X-ray and ct chest
Xuxin Chen, Ximing Wang, Kecheng Zhang, Roy Zhang, K. Fung, T. Thai, K. Moore, R. Mannel, Hong Liu, B. Zheng, Y. Qiu (2021)
Recent advances and clinical applications of deep learning in medical image analysisMedical image analysis, 79
S. Leonelli (2016)
Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systemsPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences, 374
S. Athar (2011)
Principles of Biomedical EthicsThe Journal of IMA, 43
(2022)
Multimodal MRI brain tumor segmentation—a ResNet-based U-Net approach
T. Lysaght, H. Lim, Vicki Xafis, K. Ngiam (2019)
AI-Assisted Decision-making in HealthcareAsian Bioethics Review, 11
Bjoern Menze, A. Jakab, S. Bauer, Jayashree Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, L. Lanczi, E. Gerstner, M. Weber, T. Arbel, B. Avants, N. Ayache, Patricia Buendia, D. Collins, Nicolas Cordier, Jason Corso, A. Criminisi, T. Das, H. Delingette, Çağatay Demiralp, C. Durst, M. Dojat, Senan Doyle, Joana Festa, F. Forbes, Ezequiel Geremia, B. Glocker, P. Golland, Xiaotao Guo, A. Hamamci, K. Iftekharuddin, R. Jena, N. John, E. Konukoglu, D. Lashkari, J. Mariz, Raphael Meier, Sérgio Pereira, Doina Precup, S. Price, Tammy Riklin-Raviv, Syed Reza, Michael Ryan, Duygu Sarikaya, L. Schwartz, Hoo-Chang Shin, J. Shotton, Carlos Silva, N. Sousa, N. Subbanna, G. Székely, Thomas Taylor, O. Thomas, N. Tustison, Gözde Ünal, F. Vasseur, M. Wintermark, Dong Ye, Liang Zhao, Binsheng Zhao, D. Zikic, M. Prastawa, M. Reyes, K. Leemput (2015)
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)IEEE Transactions on Medical Imaging, 34
M. Yaffe (2019)
Emergence of "Big Data" and Its Potential and Current Limitations in Medical Imaging.Seminars in nuclear medicine, 49 2
D. Altman (1980)
Statistics and ethics in medical research: V--Analysing data.British Medical Journal, 281
T. Kalaiselvi, S. Padmapriya, Karuppanagounder Somasundaram, R. Vasanth (2022)
A Novel Activation Function for Brain Tumor Segmentation using V-NET Approach
Thiyagarajan Padmapriya, Kalaiselvi Thiruvenkadam, Venugopal Priyadharshini (2022)
Multimodal covid network: Multimodal bespoke convolutional neural network architectures for COVID‐19 detection from chest X‐ray's and computerized tomography scansInternational Journal of Imaging Systems and Technology, 32
J. Saltz, Neil Dewar (2019)
Data science ethical considerations: a systematic literature review and proposed project frameworkEthics and Information Technology
J. Weese, C. Lorenz (2016)
Four challenges in medical image analysis from an industrial perspectiveMedical image analysis, 33
Dave Martens, A. Delaigle, P. Gustafson (2023)
Handbook of Measurement Error Models
Vicki Xafis, Markus Labude (2019)
Openness in Big Data and Data RepositoriesAsian Bioethics Review, 11
W. Lipworth (2019)
Real-world Data to Generate Evidence About Healthcare InterventionsAsian Bioethics Review, 11
David Martens (2022)
Data Science Ethics
T. Kalaiselvi, S. Padmapriya, P. Sriramakrishnan, Karuppanagounder Somasundaram (2020)
Deriving tumor detection models using convolutional neural networks from MRI of human brain scansInternational Journal of Information Technology, 12
D. Shen, Guorong Wu, Heung-Il Suk (2017)
Deep Learning in Medical Image Analysis.Annual review of biomedical engineering, 19
F. Altaf, Syed Islam, Naveed Akhtar, N. Janjua (2019)
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future DirectionsIEEE Access, 7
T. Kalaiselvi, S. Padmapriya, Karuppanagounder Somasundaram, P. Sriramakrishnan (2021)
A deep learning approach for brain tumour detection system using convolutional neural networksInternational Journal of Dynamical Systems and Differential Equations
T Kalaiselvi (2021)
10.1504/IJDSDE.2021.120046International Journal of Dynamical Systems and Differential Equations, 11
U. Sivarajah, M. Kamal, Z. Irani, V. Weerakkody (2017)
Critical analysis of Big Data challenges and analytical methodsJournal of Business Research, 70
Vicki Xafis, G. Schaefer, Markus Labude, I. Brassington, Angela Ballantyne, H. Lim, W. Lipworth, T. Lysaght, C. Stewart, S. Sun, G. Laurie, E. Tai (2019)
Cross-Sectoral Big DataAsian Bioethics Review, 11
Shouvik Chakraborty, Kalyani Mali (2020)
An Overview of Biomedical Image Analysis From the Deep Learning Perspective
T. Beauchamp (2003)
Methods and principles in biomedical ethicsJournal of Medical Ethics, 29
A. Dawson (2009)
Theory and practice in public health ethics: a complex relationship
G. Schaefer, E. Tai, S. Sun (2019)
Precision Medicine and Big DataAsian Bioethics Review, 11
T. Habuza, A. Navaz, Faiza Hashim, F. Alnajjar, Nazar Zaki, M. Serhani, Y. Statsenko (2021)
AI applications in robotics, diagnostic image analysis and precision medicine: Current limitations, future trends, guidelines on CAD systems for medicineInformatics in Medicine Unlocked, 24
T. Kalaiselvi, Thiyagarajan Padmapriya, P. Sriramakrishnan, Venugopal Priyadharshini (2020)
Development of automatic glioma brain tumor detection system using deep convolutional neural networksInternational Journal of Imaging Systems and Technology, 30
Clarissa Martin, Kyle DeStefano, Harry Haran, S. Zink, J. Dai, D. Ahmed, Abrahim Razzak, K. Lin, Ann Kogler, J. Waller, K. Kazmi, Muhammad Umair (2022)
The ethical considerations including inclusion and biases, data protection, and proper implementation among AI in radiology and potential implicationsIntelligence-Based Medicine
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Angela Ballantyne, C. Stewart (2019)
Big Data and Public-Private Partnerships in Healthcare and ResearchAsian Bioethics Review, 11
Due to advancements in technology such as data science and artificial intelligence, healthcare research has gained momentum and is generating new findings and predictions on abnormalities leading to the diagnosis of diseases or disorders in human beings. On one hand, the extensive application of data science to healthcare research is progressing faster, while on the other hand, the ethical concerns and adjoining risks and legal hurdles those data scientists may face in the future slow down the progression of healthcare research. Simply put, the application of data science to ethically guided healthcare research appears to be a dream come true. Hence, in this paper, we discuss the current practices, challenges, and limitations of the data collection process during medical image analysis (MIA) conducted as part of healthcare research and propose an ethical data collection framework to guide data scientists to address the possible ethical concerns before commencing data analytics over a medical dataset.
Asian Bioethics Review – Springer Journals
Published: Jan 1, 2024
Keywords: Data ethics; Medical imaging; Data collection; Data science; Research ethics; Data privacy; Data analytics
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.