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The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning.
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Black box integration of computer-aided diagnosis into pacs deserves a second chance: results of a usability study concerning bone age assessment
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W3C · 2013
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W3C Working Group · 2013
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Standardization of analysis sets for reporting results from adni mri data
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Evaluation of prostate segmentation algorithms for mri: the promise12 challenge
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The multimodal brain tumor image segmentation benchmark (brats)
Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, et al · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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False discovery rates in pet and ct studies with texture features: a systematic review
Anastasia Chalkidou, Michael J O’Doherty, and Paul K Marsden · 2015
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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod): the tripod statement
Gary S Collins, Johannes B Reitsma, Douglas G Altman, and Karel GM Moons · 2015
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Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer
Xia Li, Richard G Abramson, Lori R Arlinghaus, Hakmook Kang, Anuradha Bapsi Chakravarthy, Vandana G Abramson, Jaime Farley, Ingrid A Mayer, Mark C Kelley, Ingrid M Meszoely, et al · 2015
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Problems and preferences in pediatric imaging
Brij Bhushan Thukral · 2015
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Applying Human Factors and Usability Engineering to Medical Devices, 2016
FDA · 2016
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What does research reproducibility mean?
Steven N Goodman, Daniele Fanelli, and John PA Ioannidis · 2016
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The ethics of algorithms: Mapping the debate
Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi · 2016
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Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric mri
Ke Nie, Liming Shi, Qin Chen, Xi Hu, Salma K Jabbour, Ning Yue, Tianye Niu, and Xiaonan Sun · 2016
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‘ why should i trust you?’
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2016
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OJ , L 117:1––175, 2020-04-24
Regulation (eu) 2017/745 of the european parliament and of the council of 5 april 2017 on medical devices, amending directive 2001/83/ec, regulation (ec) no 178/2002 and regulation (ec) no 1223/2009 and repealing council directives 90/385/eec and 93/42/eec · 2017
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Artificial intelligence
ANSI · 2017
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Mitigating poisoning attacks on machine learning models: A data provenance based approach
Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, and Jaehoon Amir Safavi · 2017
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Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Harmonization of multi-site diffusion tensor imaging data
Jean-Philippe Fortin, Drew Parker, Birkan Tunç, Takanori Watanabe, Mark A Elliott, Kosha Ruparel, David R Roalf, Theodore D Satterthwaite, Ruben C Gur, Raquel E Gur, et al · 2017
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Socioeconomic and demographic predictors of missed opportunities to provide advanced imaging services
McKinley Glover IV, Dania Daye, Omid Khalilzadeh, Oleg Pianykh, Daniel I Rosenthal, James A Brink, and Efrén J Flores · 2017
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A survey on provenance: What for? what form? what from?
Melanie Herschel, Ralf Diestelkämper, and Houssem Ben Lahmar · 2017
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Radiomics: the bridge between medical imaging and personalized medicine
Philippe Lambin, Ralph TH Leijenaar, Timo M Deist, Jurgen Peerlings, Evelyn EC De Jong, Janita Van Timmeren, Sebastian Sanduleanu, Ruben THM Larue, Aniek JG Even, Arthur Jochems, et al · 2017
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The doctor just won’t accept that!
Zachary C Lipton · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
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Automatically tracking metadata and provenance of machine learning experiments
Sebastian Schelter, Joos-Hendrik Boese, Johannes Kirschnick, Thoralf Klein, and Stephan Seufert · 2017
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A deep cascade of convolutional neural networks for dynamic mr image reconstruction
Jo Schlemper, Jose Caballero, Joseph V Hajnal, Anthony N Price, and Daniel Rueckert · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Asilomar AI Principles; Principles developed in conjunction with the 2017 Asilomar conference 2017, 2017
THE FUTURE OF LIFE INSTITUTE (FLI) · 2017
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The alzheimer’s disease neuroimaging initiative 3: Continued innovation for clinical trial improvement
Michael W Weiner, Dallas P Veitch, Paul S Aisen, Laurel A Beckett, Nigel J Cairns, Robert C Green, Danielle Harvey, Clifford R Jack Jr, William Jagust, John C Morris, et al · 2017
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Diagnosing machine learning pipelines with fine-grained lineage
Zhao Zhang, Evan R Sparks, and Michael J Franklin · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Quantitative variations in texture analysis features dependent on mri scanning parameters: A phantom model
Karen Buch, Hirofumi Kuno, Muhammad M Qureshi, Baojun Li, and Osamu Sakai · 2018
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This looks like that: deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin · 2018
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Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford · 2018
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Regression concept vectors for bidirectional explanations in histopathology
Mara Graziani, Vincent Andrearczyk, and Henning Müller · 2018
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Mr imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy
Natally Horvat, Harini Veeraraghavan, Monika Khan, Ivana Blazic, Junting Zheng, Marinela Capanu, Evis Sala, Julio Garcia-Aguilar, Marc J Gollub, and Iva Petkovska · 2018
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Iso 9241-11:2018. ergonomics of human-system interaction — part 11: Usability: Definitions and concepts
ISO · 2018
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A fully adaptive image classification approach for industrial revolution 4.0
Syed Muslim Jameel, Manzoor Ahmed Hashmani, Hitham Alhussain, and Arif Budiman · 2018
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To trust or not to trust a classifier
Heinrich Jiang, Been Kim, Melody Y Guan, and Maya Gupta · 2018
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On the effect of inter-observer variability for a reliable estimation of uncertainty of medical image segmentation
Alain Jungo, Raphael Meier, Ekin Ermis, Marcela Blatti-Moreno, Evelyn Herrmann, Roland Wiest, and Mauricio Reyes · 2018
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Quantitative magnetic resonance imaging phantoms: a review and the need for a system phantom
Kathryn E Keenan, Maureen Ainslie, Alex J Barker, Michael A Boss, Kim M Cecil, Cecil Charles, Thomas L Chenevert, Larry Clarke, Jeffrey L Evelhoch, Paul Finn, et al · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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Esr paper on structured reporting in radiology
European Society of Radiology (ESR) communications@ myesr. org · 2018
Cited alongside, same era.
Phase recovery and holographic image reconstruction using deep learning in neural networks
Yair Rivenson, Yibo Zhang, Harun Günaydın, Da Teng, and Aydogan Ozcan · 2018
Cited alongside, same era.
“meaningful information” and the right to explanation
Andrew Selbst and Julia Powles · 2018
Cited alongside, same era.
Accelerating the machine learning lifecycle with mlflow
Matei Zaharia, Andrew Chen, Aaron Davidson, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe, et al · 2018
Cited alongside, same era.
Novel imaging phantom for accurate and robust measurement of brain atrophy rates using clinical mri
Houshang Amiri, Iman Brouwer, Joost PA Kuijer, Jan C de Munck, Frederik Barkhof, and Hugo Vrenken · 2019
Cited alongside, same era.
Image-based cardiac diagnosis with machine learning: a review
Carlos Martin-Isla, Victor M Campello, Cristian Izquierdo, Zahra Raisi-Estabragh, Bettina Baeßler, Steffen E Petersen, and Karim Lekadir · 2020
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International evaluation of an ai system for breast cancer screening
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg S Corrado, Ara Darzi, et al · 2020
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Standardization in quantitative imaging: a multicenter comparison of radiomic features from different software packages on digital reference objects and patient data sets
M McNitt-Gray, S Napel, A Jaggi, SA Mattonen, L Hadjiiski, M Muzi, D Goldgof, Y Balagurunathan, LA Pierce, PE Kinahan, et al · 2020
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Artificial intelligence in low-and middle-income countries: innovating global health radiology
Daniel J Mollura, Melissa P Culp, Erica Pollack, Gillian Battino, John R Scheel, Victoria L Mango, Ameena Elahi, Alan Schweitzer, and Farouk Dako · 2020
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Artificial intelligence: Who is responsible for the diagnosis?, 2020
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Matthew Arnold, Rachel KE Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilović, Ravi Nair, K Natesan Ramamurthy, Alexandra Olteanu, David Piorkowski, et al · 2019
Cited alongside, same era.
Ai fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilović, et al · 2019
Cited alongside, same era.
Reporting of artificial intelligence prediction models
Gary S Collins and Karel GM Moons · 2019
Cited alongside, same era.
Radiomics model to predict early progression of nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy: a multicenter study
Richard Du, Victor H Lee, Hui Yuan, Ka-On Lam, Herbert H Pang, Yu Chen, Edmund Y Lam, Pek-Lan Khong, Anne W Lee, Dora L Kwong, et al · 2019
Cited alongside, same era.
Testing the robustness of attribution methods for convolutional neural networks in mri-based alzheimer’s disease classification
Fabian Eitel, Kerstin Ritter, Alzheimer’s Disease Neuroimaging Initiative (ADNI, et al · 2019
Cited alongside, same era.
Ethics guidelines for trustworthy ai. 2019., 2019
EU Commission · 2019
Cited alongside, same era.
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
Livia Faes, Siegfried K Wagner, Dun Jack Fu, Xiaoxuan Liu, Edward Korot, Joseph R Ledsam, Trevor Back, Reena Chopra, Nikolas Pontikos, Christoph Kern, et al · 2019
Cited alongside, same era.
Emanuele Neri, Francesca Coppola, Vittorio Miele, Corrado Bibbolino, and Roberto Grassi · 2020
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A deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images
Harsh Panwar, PK Gupta, Mohammad Khubeb Siddiqui, Ruben Morales-Menendez, Prakhar Bhardwaj, and Vaishnavi Singh · 2020
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Evaluating artificial intelligence in medicine: phases of clinical research
Yoonyoung Park, Gretchen Purcell Jackson, Morgan A Foreman, Daniel Gruen, Jianying Hu, and Amar K Das · 2020
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Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities
Jessica K Paulus and David M Kent · 2020
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Deep structural causal models for tractable counterfactual inference
Nick Pawlowski, Daniel C Castro, and Ben Glocker · 2020
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Continuous learning ai in radiology: implementation principles and early applications
Oleg S Pianykh, Georg Langs, Marc Dewey, Dieter R Enzmann, Christian J Herold, Stefan O Schoenberg, and James A Brink · 2020
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Co-rads: a categorical ct assessment scheme for patients suspected of having covid-19—definition and evaluation
Mathias Prokop, Wouter Van Everdingen, Tjalco van Rees Vellinga, Henriëtte Quarles van Ufford, Lauran Stöger, Ludo Beenen, Bram Geurts, Hester Gietema, Jasenko Krdzalic, Cornelia Schaefer-Prokop, et al · 2020
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Interpretable deep models for cardiac resynchronisation therapy response prediction
Esther Puyol-Antón, Chen Chen, James R Clough, Bram Ruijsink, Baldeep S Sidhu, Justin Gould, Bradley Porter, Marc Elliott, Vishal Mehta, Daniel Rueckert, et al · 2020
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Increased power by harmonizing structural mri site differences with the combat batch adjustment method in enigma
Joaquim Radua, Eduard Vieta, Russell Shinohara, Peter Kochunov, Yann Quidé, Melissa J Green, Cynthia S Weickert, Thomas Weickert, Jason Bruggemann, Tilo Kircher, et al · 2020
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Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations
Michael P Recht, Marc Dewey, Keith Dreyer, Langlotz Curtis, Niessen Wiro, Barbara Prainsack, and John J Smith · 2020
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On the interpretability of artificial intelligence in radiology: challenges and opportunities
Mauricio Reyes, Raphael Meier, Sérgio Pereira, Carlos A Silva, Fried-Michael Dahlweid, Hendrik von Tengg-Kobligk, Ronald M Summers, and Roland Wiest · 2020
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Mrqy—an open-source tool for quality control of mr imaging data
Amir Reza Sadri, Andrew Janowczyk, Ren Zhou, Ruchika Verma, Niha Beig, Jacob Antunes, Anant Madabhushi, Pallavi Tiwari, and Satish E Viswanath · 2020
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Qc-automator: Deep learning-based automated quality control for diffusion mr images
Zahra Riahi Samani, Jacob Antony Alappatt, Drew Parker, Abdol Aziz Ould Ismail, and Ragini Verma · 2020
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Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images
Philipp Seegerer, Alexander Binder, René Saitenmacher, Michael Bockmayr, Maximilian Alber, Philipp Jurmeister, Frederick Klauschen, and Klaus-Robert Müller · 2020
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Chexclusion: Fairness gaps in deep chest x-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y Chen, and Marzyeh Ghassemi · 2020
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Radiological society of north america expert consensus document on reporting chest ct findings related to covid-19: endorsed by the society of thoracic radiology, the american college of radiology, and rsna
Scott Simpson, Fernando U Kay, Suhny Abbara, Sanjeev Bhalla, Jonathan H Chung, Michael Chung, Travis S Henry, Jeffrey P Kanne, Seth Kligerman, Jane P Ko, et al · 2020
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Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology
Martina Sollini, Francesco Bartoli, Andrea Marciano, Roberta Zanca, Riemer HJA Slart, and Paola A Erba · 2020
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A review of original articles published in the emerging field of radiomics
Jiangdian Song, Yanjie Yin, Hairui Wang, Zhihui Chang, Zhaoyu Liu, and Lei Cui · 2020
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Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey N Chiang, Zhihao Wu, and Xiaowei Ding · 2020
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Unsupervised mri homogenization: Application to pediatric anterior visual pathway segmentation
Carlos Tor-Diez, Antonio Reyes Porras, Roger J Packer, Robert A Avery, and Marius George Linguraru · 2020
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Human–computer collaboration for skin cancer recognition
Philipp Tschandl, Christoph Rinner, Zoe Apalla, Giuseppe Argenziano, Noel Codella, Allan Halpern, Monika Janda, Aimilios Lallas, Caterina Longo, Josep Malvehy, et al · 2020
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Temporal changes of ct findings in 90 patients with covid-19 pneumonia: a longitudinal study
Yuhui Wang, Chengjun Dong, Yue Hu, Chungao Li, Qianqian Ren, Xin Zhang, Heshui Shi, and Min Zhou · 2020
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Preparing medical imaging data for machine learning
Martin J Willemink, Wojciech A Koszek, Cailin Hardell, Jie Wu, Dominik Fleischmann, Hugh Harvey, Les R Folio, Ronald M Summers, Daniel L Rubin, and Matthew P Lungren · 2020
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Basic documents
World Health Organization · 2020
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Explainability for regression cnn in fetal head circumference estimation from ultrasound images
Jing Zhang, Caroline Petitjean, Florian Yger, and Samia Ainouz · 2020
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Alex Zwanenburg, Martin Vallières, Mahmoud A Abdalah, Hugo JWL Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Spyridon Bakas, Roelof J Beukinga, Ronald Boellaard, et al · 2020
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OJ , 2021-04-21
Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS COM/2021/206 final · 2021
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Reading race: Ai recognises patient’s racial identity in medical images
Imon Banerjee, Ananth Reddy Bhimireddy, John L Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, et al · 2021
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On the role of artificial intelligence in medical imaging of covid-19
Jannis Born, David Beymer, Deepta Rajan, Adam Coy, Vandana V Mukherjee, Matteo Manica, Prasanth Prasanna, Deddeh Ballah, Michal Guindy, Dorith Shaham, et al · 2021
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Transparency, auditability, and explainability of machine learning models in credit scoring
Michael Bücker, Gero Szepannek, Alicja Gosiewska, and Przemyslaw Biecek · 2021
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Multi-centre, multi-vendor and multi-disease cardiac segmentation: The m&ms challenge
Víctor M Campello, Polyxeni Gkontra, Cristian Izquierdo, Carlos Martín-Isla, Alireza Sojoudi, Peter M Full, Klaus Maier-Hein, Yao Zhang, Zhiqiang He, Jun Ma, et al · 2021
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Concept drift detection and adaptation for federated and continual learning
Fernando E Casado, Dylan Lema, Marcos F Criado, Roberto Iglesias, Carlos V Regueiro, and Senén Barro · 2021
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A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: High grade vs. low grade
T JM Castillo, MPA Starmans, M Arif, WJ Niessen, Stefan Klein, Chris H Bangma, Ivo G Schoots, and JF Veenland · 2021
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Ai for radiographic covid-19 detection selects shortcuts over signal
Alex J DeGrave, Joseph D Janizek, and Su-In Lee · 2021
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Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools
Oliver Diaz, Kaisar Kushibar, Richard Osuala, Akis Linardos, Lidia Garrucho, Laura Igual, Petia Radeva, Fred Prior, Polyxeni Gkontra, and Karim Lekadir · 2021
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Usability evaluation of selected picture archiving and communication systems at the national level: Analysis of users’ viewpoints
Mehrdad Farzandipour, Monireh Sadeqi Jabali, Ali Mohammad Nickfarjam, and Hamidreza Tadayon · 2021
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Proposed Rregulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based-Software as a Medical Device (SaMD), 2021
FDA · 2021
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Determining breast cancer biomarker status and associated morphological features using deep learning
Paul Gamble, Ronnachai Jaroensri, Hongwu Wang, Fraser Tan, Melissa Moran, Trissia Brown, Isabelle Flament-Auvigne, Emad A Rakha, Michael Toss, David J Dabbs, et al · 2021
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Automated end-to-end management of the modeling lifecycle in deep learning
Gharib Gharibi, Vijay Walunj, Raju Nekadi, Raj Marri, and Yugyung Lee · 2021
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Understanding pi-qual for prostate mri quality: a practical primer for radiologists
Francesco Giganti, Alex Kirkham, Veeru Kasivisvanathan, Marianthi-Vasiliki Papoutsaki, Shonit Punwani, Mark Emberton, Caroline M Moore, and Clare Allen · 2021
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Adrian Hoffmann, Claudio Fanconi, Rahul Rade, and Jonas Kohler · 2021
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Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence-based image analysis
Isamu Hoshino and Hajime Yokota · 2021
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Repeatability and reproducibility study of radiomic features on a phantom and human cohort
AK Jha, S Mithun, V Jaiswar, UB Sherkhane, NC Purandare, K Prabhash, V Rangarajan, A Dekker, L Wee, and A Traverso · 2021
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Test-time adaptable neural networks for robust medical image segmentation
Neerav Karani, Ertunc Erdil, Krishna Chaitanya, and Ender Konukoglu · 2021
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Non-small cell lung carcinoma histopathological subtype phenotyping using high-dimensional multinomial multiclass ct radiomics signature
Zahra Khodabakhshi, Shayan Mostafaei, Hossein Arabi, Mehrdad Oveisi, Isaac Shiri, and Habib Zaidi · 2021
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Regulatory frameworks for development and evaluation of artificial intelligence–based diagnostic imaging algorithms: summary and recommendations
David B Larson, Hugh Harvey, Daniel L Rubin, Neville Irani, R Tse Justin, and Curtis P Langlotz · 2021
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Bringing ai to the clinic: blueprint for a vendor-neutral ai deployment infrastructure
Tim Leiner, Edwin Bennink, Christian P Mol, Hugo J Kuijf, and Wouter B Veldhuis · 2021
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Estimating and improving fairness with adversarial learning
Xiaoxiao Li, Ziteng Cui, Yifan Wu, Li Gu, and Tatsuya Harada · 2021
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Explainable ai: A review of machine learning interpretability methods
Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis · 2021
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Multicenter dsc–mri-based radiomics predict idh mutation in gliomas
Georgios C Manikis, Georgios S Ioannidis, Loizos Siakallis, Katerina Nikiforaki, Michael Iv, Diana Vozlic, Katarina Surlan-Popovic, Max Wintermark, Sotirios Bisdas, and Kostas Marias · 2021
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Traceability for trustworthy ai: A review of models and tools
Marçal Mora-Cantallops, Salvador Sánchez-Alonso, Elena García-Barriocanal, and Miguel-Angel Sicilia · 2021
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