Dermatologist-level classification of skin cancer with deep neural networks

Esteva, Andre, Kuprel, Brett, Novoa, Roberto A., Ko, Justin, Swetter, Susan M., Blau, Helen M., Thrun, Sebastian·2017·Nature
DOI 10.1038/nature21056

Cited by (1,225)

Items 101–200 of 1,225

  1. Artificial intelligence in medical practice: the question to the answer?
    Miller, D. Douglas, Brown, Eric W. · 2018 · The American Journal of Medicine
  2. Deep neural network models for computational histopathology: A survey
    Srinidhi, Chetan L., Ciga, Ozan, Martel, Anne L. · 2021 · Medical Image Analysis
  3. AI can be sexist and racist — it’s time to make it fair
    Zou, James, Schiebinger, Londa · 2018 · Nature
  4. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
    Hirasawa, Toshiaki, Aoyama, Kazuharu, Tanimoto, Tetsuya, Ishihara, Soichiro, Shichijo, Satoki, Ozawa, Tsuyoshi, Ohnishi, Tatsuya, Fujishiro, Mitsuhiro, Matsuo, Keigo, Fujisaki, Junko et al. · 2018 · Gastric Cancer
  5. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
    Cruz Rivera, Samantha, Liu, Xiaoxuan, Chan, An-Wen, Denniston, Alastair K., Calvert, Melanie J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, Ara, Holmes, Christopher, Yau, Christopher et al. · 2020 · Nature Medicine
  6. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
    Bien, Nicholas, Rajpurkar, Pranav, Ball, Robyn L., Irvin, Jeremy, Park, Allison, Jones, Erik, Bereket, Michael, Patel, Bhavik N., Yeom, Kristen W., Shpanskaya, Katie et al. · 2018 · PLOS Medicine
  7. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy
    Gregor Urban, Priyam V. Tripathi, Talal Alkayali, Mohit Mittal, Farid Jalali, William E. Karnes, Pierre Baldi · 2018 · Gastroenterology
  8. Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks.
    Basaia, Silvia, Agosta, Federica, Wagner, Luca, Canu, Elisa, Magnani, Giuseppe, Santangelo, Roberto, Filippi, Massimo · 2019 · NeuroImage: Clinical
  9. Transfusion: Understanding Transfer Learning for Medical Imaging
    Maithra Raghu, Chiyuan Zhang, Jon Michael Kleinberg, Samy Bengio A · 2019 · arXiv (Cornell University)
  10. Guidelines of care for the management of primary cutaneous melanoma
    Susan M. Swetter, Hensin Tsao, Christopher Keram Bichakjian, Clara N. Curiel‐Lewandrowski, David E. Elder, Jeffrey E. Gershenwald, Valerie Guild, Jane Margaret Grant-Kels, Allan C. Halpern, Timothy M. Johnson et al. · 2018 · Journal of the American Academy of Dermatology
  11. Multimodal deep learning models for early detection of Alzheimer’s disease stage
    Venugopalan, Janani, Tong, Li, Hassanzadeh, Hamid Reza, Wang, May D. · 2021 · Scientific Reports
  12. Cutaneous melanoma
    Georgina Venetia Long, Susan M. Swetter, Alexander Maxwell Menzies, Jeffrey E. Gershenwald, Richard A. Scolyer · 2023 · The Lancet
  13. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success
    Thrall, James H., Li, Xiang, Li, Quanzheng, Cruz, Cinthia, Do, Synho, Dreyer, Keith, Brink, James · 2018 · Journal of the American College of Radiology
  14. Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
    Davood Karimi, Haoran Dou, Simon Keith Warfield, Ali Gholipour · 2020 · Medical Image Analysis
  15. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
    Bai, Wenjia, Sinclair, Matthew, Tarroni, Giacomo, Oktay, Ozan, Rajchl, Martin, Vaillant, Ghislain, Lee, Aaron M., Aung, Nay, Lukaschuk, Elena, Sanghvi, Mihir M. et al. · 2018 · Journal of Cardiovascular Magnetic Resonance
  16. Smart healthcare: making medical care more intelligent
    Shuo Tian, Wenbo Yang, Jehane Michael Le Grange, Peng Wang, Wei Huang, Zhewei Ye · 2019 · Global Health Journal
  17. Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm
    Seung Seog Han, Myoung Shin Kim, Woohyung Lim, Gyeong‐Hun Park, Ilwoo Park, Sung Eun Chang · 2018 · Journal of Investigative Dermatology
  18. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
    Bulten, Wouter, Pinckaers, Hans, van Boven, Hester, Vink, Robert, de Bel, Thomas, van Ginneken, Bram, van der Laak, Jeroen, Hulsbergen-van de Kaa, Christina, Litjens, Geert · 2020 · The Lancet Oncology
  19. Artificial intelligence in retina
    Schmidt-Erfurth, Ursula, Sadeghipour, Amir, Gerendas, Bianca S., Waldstein, Sebastian M., Bogunović, Hrvoje · 2018 · Progress in Retinal and Eye Research
  20. Deep Learning—A Technology With the Potential to Transform Health Care
    Hinton, Geoffrey · 2018 · JAMA
  21. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension
    Liu, Xiaoxuan, Rivera, Samantha Cruz, Moher, David, Calvert, Melanie J, Denniston, Alastair K · 2020 · BMJ
  22. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance.
    Yuan, Yading, Chao, Ming, Lo, Yeh-Chi · 2017 · IEEE Transactions on Medical Imaging
  23. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
    Larrazabal, Agostina J., Nieto, Nicolás, Peterson, Victoria, Milone, Diego H., Ferrante, Enzo · 2020 · Proceedings of the National Academy of Sciences
  24. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges
    Shigao Huang, Jie Yang, Simon James Fong, Qi Zhao · 2019 · Cancer Letters
  25. Artificial intelligence for clinical trial design
    Harrer, Stefan, Shah, Pratik, Antony, Bhavna, Hu, Jianying · 2019 · Trends in Pharmacological Sciences
  26. Deep learning based tissue analysis predicts outcome in colorectal cancer
    Bychkov, Dmitrii, Linder, Nina, Turkki, Riku, Nordling, Stig, Kovanen, Panu E., Verrill, Clare, Walliander, Margarita, Lundin, Mikael, Haglund, Caj, Lundin, Johan · 2018 · Scientific Reports
  27. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
    Huiying Liang, Brian Tsui, Hao Ni, Carolina C. S. Valentim, Sally L. Baxter, Guangjian Liu, Wenjia Cai, Daniel Kermany, Xin Sun, Jiancong Chen et al. · 2019 · Nature Medicine
  28. Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
    Wang, Guotai, Li, Wenqi, Aertsen, Michael, Deprest, Jan, Ourselin, Sébastien, Vercauteren, Tom · 2019 · Neurocomputing
  29. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
    Yu Fu, Alexander W. Jung, Ramón Viñas, Santiago González, Harald S. Vöhringer, Artem Shmatko, Lucy R. Yates, Mercedes Jimenez‐Liñan, Luiza Moore, Moritz Gerstung · 2020 · Nature Cancer
  30. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
    Brown, James M., Campbell, J. Peter, Beers, Andrew, Chang, Ken, Ostmo, Susan, Chan, R. V. Paul, Dy, Jennifer, Erdogmus, Deniz, Ioannidis, Stratis, Kalpathy-Cramer, Jayashree et al. · 2018 · JAMA Ophthalmology
  31. Deep learning microscopy
    Yair Rivenson, Zoltán S. Göröcs, Harun Günaydın, Yibo Zhang, Hongda Wang, Aydogan Özcan · 2017 · Optica
  32. Recognition in Terra Incognita
    Beery, Sara, Van Horn, Grant, Perona, Pietro · 2018 · Lecture Notes in Computer Science
  33. AI-based pathology predicts origins for cancers of unknown primary
    Lu, Ming Y., Chen, Tiffany Y., Williamson, Drew F. K., Zhao, Melissa, Shady, Maha, Lipkova, Jana, Mahmood, Faisal · 2021 · Nature
  34. Deep neural network improves fracture detection by clinicians
    Robert Lindsey, Aaron Daluiski, Sumit Chopra, Alexander S. Lachapelle, Michael C. Mozer, Serge Sicular, Douglas P. Hanel, Michael J. Gardner, Anurag Gupta, Robert J. Hotchkiss et al. · 2018 · Proceedings of the National Academy of Sciences
  35. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet
    Harsh Panwar, P. K. Gupta, Mohammad Khubeb Siddiqui, Rubén Morales-Menéndez, Vaishnavi Singh · 2020 · Chaos Solitons & Fractals
  36. What do we need to build explainable AI systems for the medical domain?
    Holzinger, Andreas, Biemann, Chris, Pattichis, Constantinos S., Kell, Douglas B. · 2017 · arXiv
  37. Machine Learning and Health Care Disparities in Dermatology
    Adamson, Adewole S., Smith, Avery · 2018 · JAMA Dermatology
  38. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study
    Ström, Peter, Kartasalo, Kimmo, Olsson, Henrik, Solorzano, Leslie, Delahunt, Brett, Berney, Daniel M, Bostwick, David G, Evans, Andrew J, Grignon, David J, Humphrey, Peter A et al. · 2020 · The Lancet Oncology
  39. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
    Li, Ji-Peng Olivia, Liu, Hanruo, Ting, Darren S.J., Jeon, Sohee, Chan, R.V. Paul, Kim, Judy E., Sim, Dawn A., Thomas, Peter B.M., Lin, Haotian, Chen, Youxin et al. · 2021 · Progress in Retinal and Eye Research
  40. A deep learning system for detecting diabetic retinopathy across the disease spectrum
    Dai, Ling, Wu, Liang, Li, Huating, Cai, Chun, Wu, Qiang, Kong, Hongyu, Liu, Ruhan, Wang, Xiangning, Hou, Xuhong, Liu, Yuexing et al. · 2021 · Nature Communications
  41. An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare
    Okeke Stephen, Mangal Sain, Uchenna Joseph Maduh, Do‐Un Jeong · 2019 · Journal of Healthcare Engineering
  42. Cutaneous melanoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up
    Olivier Alain Michielin, Alexander C. J. van Akkooi, Paolo A. Ascierto, Reinhard Dummer, Ulrich Keilholz · 2019 · Annals of Oncology
  43. Machine and deep learning methods for radiomics
    Michele Avanzo, Lise Wei, Joseph Stancanello, Martin Vallières, Arvind U.K. Rao, Olivier Morin, Sarah A. Mattonen, Issam El Naqa · 2020 · Medical Physics
  44. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task
    Brinker, Titus J., Hekler, Achim, Enk, Alexander H., Klode, Joachim, Hauschild, Axel, Berking, Carola, Schilling, Bastian, Haferkamp, Sebastian, Schadendorf, Dirk, Holland-Letz, Tim et al. · 2019 · European Journal of Cancer
  45. From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.
    Swanson, Kyle, Wu, Eric, Zhang, Angela, Alizadeh, Ash A., Zou, James · 2023 · Cell
  46. Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment.
    Rajula, Hema Sekhar Reddy, Verlato, Giuseppe, Manchia, Mirko, Antonucci, Nadia, Fanos, Vassilios · 2020 · Medicina
  47. An Introductory Review of Deep Learning for Prediction Models With Big Data
    Frank Emmert‐Streib, Zhen Yang, Feng Han, Shailesh Tripathi, Matthias Dehmer · 2020 · Frontiers in Artificial Intelligence
  48. Digital technologies, innovation, and skills: Emerging trajectories and challenges
    Tommaso Ciarli, Martin F. Kenney, Silvia Massini, Lucia Piscitello · 2021 · Research Policy
  49. "Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making
    Cai, Carrie J., Winter, Samantha, Steiner, David, Wilcox, Lauren, Terry, Michael · 2019 · Proceedings of the ACM on Human-Computer Interaction
  50. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
    Hosny, Ahmed, Parmar, Chintan, Coroller, Thibaud P., Grossmann, Patrick, Zeleznik, Roman, Kumar, Avnish, Bussink, Johan, Gillies, Robert J., Mak, Raymond H., Aerts, Hugo J. W. L. · 2018 · PLOS Medicine
  51. Skin cancer detection: A review using deep learning techniques
    Dildar, Mehwish, Akram, Shumaila, Irfan, Muhammad, Khan, Hikmat Ullah, Ramzan, Muhammad, Mahmood, Abdur Rehman, Alsaiari, Soliman Ayed, Saeed, Abdul Hakeem M, Alraddadi, Mohammed Olaythah, Mahnashi, Mater Hussen · 2021 · International Journal of Environmental Research and Public Health
  52. Fast and accurate view classification of echocardiograms using deep learning
    Madani, Ali, Arnaout, Ramy, Mofrad, Mohammad, Arnaout, Rima · 2018 · npj Digital Medicine
  53. Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology
    Wiens, Jenna, Shenoy, Erica S · 2018 · Clinical Infectious Diseases
  54. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis
    Cheng Jin, Weixiang Chen, Yukun Cao, Zhanwei Xu, Zimeng Tan, Xin Zhang, Lei Deng, Chuansheng Zheng, Jie Zhou, Heshui Shi et al. · 2020 · Nature Communications
  55. The need for uncertainty quantification in machine-assisted medical decision making
    Begoli, Edmon, Bhattacharya, Tanmoy, Kusnezov, Dimitri · 2019 · Nature Machine Intelligence
  56. How Machine Learning Will Transform Biomedicine
    Jeremy Goecks, Vahid Jalili, Laura M. Heiser, Joe W. Gray · 2020 · Cell
  57. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications
    Peiffer-Smadja, N., Rawson, T.M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F.-X., Birgand, G., Holmes, A.H. · 2020 · Clinical Microbiology and Infection
  58. On the ethics of algorithmic decision-making in healthcare
    Grote, Thomas, Berens, Philipp · 2020 · Journal of Medical Ethics
  59. Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy
    Krause, Jonathan, Gulshan, Varun, Rahimy, Ehsan, Karth, Peter, Widner, Kasumi, Corrado, Greg S., Peng, Lily, Webster, Dale R. · 2018 · Ophthalmology
  60. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild
    Artzai Picón, Aitor Álvarez-Gila, Maximiliam Seitz, Amaia Ortiz‐Barredo, Jone Echazarra, Alexander Johannes · 2018 · Computers and Electronics in Agriculture
  61. NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning
    M. Klausen, M. C. Jespersen, H. Nielsen, K. K. Jensen, V. Jurtz, C. Sønderby, M. Sommer, O. Winther, Morten Nielsen, Bent Petersen et al. · 2018 · bioRxiv
  62. Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
    Yan, Ke, Wang, Xiaosong, Lu, Le, Summers, Ronald M. · 2018 · Journal of medical imaging
  63. Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images
    S. Deitsch, Vincent Christlein, S. Berger, C. Buerhop-Lutz, A. Maier, F. Gallwitz, C. Riess · 2018 · Solar Energy
  64. TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays
    Xiaosong Wang, Yifan Peng, Le Lü, Zhiyong Lu, Ronald M. Summers · 2018 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  65. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study
    Tschandl, Philipp, Codella, Noel, Akay, Bengü Nisa, Argenziano, Giuseppe, Braun, Ralph P, Cabo, Horacio, Gutman, David, Halpern, Allan, Helba, Brian, Hofmann-Wellenhof, Rainer et al. · 2019 · The Lancet Oncology
  66. Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch
    Tison, Geoffrey H., Sanchez, José M., Ballinger, Brandon, Singh, Avesh, Olgin, Jeffrey E., Pletcher, Mark J., Vittinghoff, Eric, Lee, Emily S., Fan, Shannon M., Gladstone, Rachel A. et al. · 2018 · JAMA Cardiology
  67. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
    Haoxin Mai, Tu C. Le, Dehong Chen, David Alan Winkler, Rachel A. Caruso · 2022 · Chemical Reviews
  68. Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors
    Ming Wang, Zheng Yan, Ting Wang, Pingqiang Cai, Siyu Gao, Yi Zeng, Changjin Wan, Hong Wang, Liang Pan, Jiancan Yu et al. · 2020 · Nature Electronics
  69. Artificial Intelligence and Digital Pathology: Challenges and Opportunities
    Tizhoosh, Hamid Reza, Pantanowitz, Liron · 2018 · Journal of Pathology Informatics
  70. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
    Mazurowski, Maciej A., Buda, Mateusz, Saha, Ashirbani, Bashir, Mustafa R. · 2019 · Journal of Magnetic Resonance Imaging
  71. An explainable deep machine vision framework for plant stress phenotyping
    Sambuddha Ghosal, David Blystone, Asheesh Kumar Singh, Baskar Ganapathysubramanian, Arti Singh, Soumik Sarkar · 2018 · Proceedings of the National Academy of Sciences
  72. Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer
    Devvi Sarwinda, Radifa Hilya Paradisa, Alhadi Bustamam, Pinkie Anggia · 2021 · Procedia Computer Science
  73. Trusted Multi-View Classification With Dynamic Evidential Fusion
    Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou · 2022 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  74. Understanding adversarial attacks on deep learning based medical image analysis systems
    Ma, Xingjun, Niu, Yuhao, Gu, Lin, Wang, Yisen, Zhao, Yitian, Bailey, James, Lu, Feng · 2021 · Pattern Recognition
  75. Deep learning-based classification of mesothelioma improves prediction of patient outcome
    Courtiol, Pierre, Maussion, Charles, Moarii, Matahi, Pronier, Elodie, Pilcer, Samuel, Sefta, Meriem, Manceron, Pierre, Toldo, Sylvain, Zaslavskiy, Mikhail, Le Stang, Nolwenn et al. · 2019 · Nature Medicine
  76. Disparities in dermatology AI performance on a diverse, curated clinical image set
    Daneshjou, Roxana, Vodrahalli, Kailas, Novoa, Roberto A., Jenkins, Melissa, Liang, Weixin, Rotemberg, Veronica, Ko, Justin, Swetter, Susan M., Bailey, Elizabeth E., Gevaert, Olivier et al. · 2022 · Science Advances
  77. Deep learning in ophthalmology: The technical and clinical considerations
    Daniel Shu Wei Ting, Lily H. Peng, Avinash V. Varadarajan, Pearse Andrew Keane, Philippe Burlina, Michael F. Chiang, Leopold F. Schmetterer, Louis Robert Pasquale, Neil M. Bressler, Dale R. Webster et al. · 2019 · Progress in Retinal and Eye Research
  78. Machine learning in healthcare
    Habehh, Hafsa, Gohel, Suril · 2021 · Current Genomics
  79. Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal
    Guan, Steven, Khan, Amir A., Sikdar, Siddhartha, Chitnis, Parag V. · 2020 · IEEE Journal of Biomedical and Health Informatics
  80. Do as AI say: susceptibility in deployment of clinical decision-aids
    Gaube, Susanne, Suresh, Harini, Raue, Martina, Merritt, Alexander, Berkowitz, Seth J., Lermer, Eva, Coughlin, Joseph F., Guttag, John V., Colak, Errol, Ghassemi, Marzyeh · 2021 · npj Digital Medicine
  81. Membership Inference Attacks From First Principles
    Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr · 2022 · IEEE Symposium on Security and Privacy (S&P)
  82. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
    Liu, Xiaoxuan, Cruz Rivera, Samantha, Moher, David, Calvert, Melanie J, Denniston, Alastair K, Ashrafian, Hutan, Beam, Andrew L, Chan, An-Wen, Collins, Gary S, Deeks, Ara DarziJonathan J et al. · 2020 · The Lancet Digital Health
  83. Second opinion needed: Communicating uncertainty in medical machine learning
    Kompa, Benjamin, Snoek, Jasper, Beam, Andrew L. · 2021 · npj Digital Medicine
  84. The emergence of AI-based wearable sensors for digital health technology: a review
    Shajari, Shaghayegh, Kuruvinashetti, Kirankumar, Komeili, Amin, Sundararaj, Uttandaraman · 2023 · Sensors
  85. Deep Learning for the Radiographic Detection of Periodontal Bone Loss
    Joachim Krois, Thomas Ekert, Leonie Meinhold, Tatiana Golla, Basel Kharbot, Agnes Wittemeier, Christof Edmund Dörfer, Falk Schwendicke · 2019 · Scientific Reports
  86. Understanding the Effect of Accuracy on Trust in Machine Learning Models
    Yin, Ming, Wortman Vaughan, Jennifer, Wallach, Hanna · 2019 · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
  87. Why digital medicine depends on interoperability
    Lehne, Moritz, Sass, Julian, Essenwanger, Andrea, Schepers, Josef, Thun, Sylvia · 2019 · npj Digital Medicine
  88. A gentle introduction to deep learning in medical image processing
    Andreas Maier, Christopher Syben, Tobias Lasser, Christian Rieß · 2019 · Zeitschrift für Medizinische Physik
  89. Convolutional neural networks for dental image diagnostics: A scoping review
    Falk Schwendicke, Tatiana Golla, Martin Dreher, Joachim Krois · 2019 · Journal of Dentistry
  90. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction
    Chinmay Belthangady, Löıc A. Royer · 2019 · Nature Methods
  91. Deep Learning and Medical Diagnosis: A Review of Literature
    Mihalj Bakator, Dragica Radosav · 2018 · Multimodal Technologies and Interaction
  92. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
    Nagpal, Kunal, Foote, Davis, Liu, Yun, Chen, Po-Hsuan Cameron, Wulczyn, Ellery, Tan, Fraser, Olson, Niels, Smith, Jenny L., Mohtashamian, Arash, Wren, James H. et al. · 2019 · npj Digital Medicine
  93. Deep learning for image-based cancer detection and diagnosis − A survey
    Zilong Hu, Jinshan Tang, Ziming Wang, Kai Zhang, Ling Zhang, Qingling Sun · 2018 · Pattern Recognition
  94. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm
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  95. Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review
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  97. Artificial Intelligence and Personalized Medicine
    Nicholas J. Schork · 2019 · Cancer treatment and research
  98. Deep learning interpretation of echocardiograms
    Ghorbani, Amirata, Ouyang, David, Abid, Abubakar, He, Bryan, Chen, Jonathan H., Harrington, Robert A., Liang, David H., Ashley, Euan A., Zou, James Y. · 2020 · npj Digital Medicine
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  100. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks
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