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

Cited by (1,225)

Items 301–400 of 1,225

  1. Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
    Brynjolfsson, Erik, Hui, Xiang, Liu, Meng · 2019 · Management Science
  2. Automated pavement crack detection and segmentation based on two-step convolutional neural network
    Jingwei Liu, Xu Yang, Stephen Lau, Xin Wang, Sang Luo, Vincent C. S. Lee, Ling Ding · 2020 · Computer-Aided Civil and Infrastructure Engineering
  3. The Application of Deep Learning in Cancer Prognosis Prediction
    Wan Zhu, Longxiang Xie, Jianye Han, Xiangqian Guo · 2020 · Cancers
  4. Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study
    Sui Peng, Yihao Liu, Weiming Lv, Longzhong Liu, Qian Zhou, Hong Yang, Jie Ren, Guangjian Liu, Xiaodong Wang, Xuehua Zhang et al. · 2021 · The Lancet Digital Health
  5. Deep learning in mental health outcome research: a scoping review
    Su, Chang, Xu, Zhenxing, Pathak, Jyotishman, Wang, Fei · 2020 · Translational Psychiatry
  6. Accelerating Federated Learning via Momentum Gradient Descent
    Liu, Wei, Chen, Li, Chen, Yunfei, Zhang, Wenyi · 2020 · IEEE Transactions on Parallel and Distributed Systems
  7. Computer-aided detection in chest radiography based on artificial intelligence: a survey
    Chunli Qin, Demin Yao, Yonghong Shi, Zhijian Song · 2018 · BioMedical Engineering OnLine
  8. Classification of skin lesions using transfer learning and augmentation with Alex-net
    Khalid Mohamed Hosny, Mohamed A. A. Kassem, Mohamed M. Foaud · 2019 · PLoS ONE
  9. Deep learning for real-time gravitational wave detection and parameter estimation: results with advanced LIGO data
    George, Daniel, Huerta, E.A. · 2018 · Physics Letters B
  10. Artificial intelligence for diabetic retinopathy screening: a review
    Grzybowski, Andrzej, Brona, Piotr, Lim, Gilbert, Ruamviboonsuk, Paisan, Tan, Gavin S. W., Abramoff, Michael, Ting, Daniel S. W. · 2020 · Eye
  11. Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks
    Dong Liang, Jing Cheng, Ziwen Ke, Leslie L. Ying · 2020 · IEEE Signal Processing Magazine
  12. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review
    Shen, Jiayi, Zhang, Casper J P, Jiang, Bangsheng, Chen, Jiebin, Song, Jian, Liu, Zherui, He, Zonglin, Wong, Sum Yi, Fang, Po-Han, Ming, Wai-Kit · 2019 · JMIR Medical Informatics
  13. Medical artificial intelligence readiness scale for medical students (MAIRS-MS)–development, validity and reliability study
    Karaca, Ozan, Çalışkan, S. Ayhan, Demir, Kadir · 2021 · BMC Medical Education
  14. NoScope: optimizing neural network queries over video at scale
    Kang, Daniel, Emmons, John, Abuzaid, Firas, Bailis, Peter, Zaharia, Matei · 2017 · Proceedings of the VLDB Endowment
  15. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
    Langlotz, Curtis P., Allen, Bibb, Erickson, Bradley J., Kalpathy-Cramer, Jayashree, Bigelow, Keith, Cook, Tessa S., Flanders, Adam E., Lungren, Matthew P., Mendelson, David S., Rudie, Jeffrey D. et al. · 2019 · Radiology
  16. Deep Learning in Medical Imaging
    Mingyu Kim, Jihye Yun, Yongwon Cho, Keewon Shin, Ryoungwoo Jang, Hyun‐Jin Bae, Namkug Kim · 2019 · Neurospine
  17. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition
    Winkler, Julia K., Fink, Christine, Toberer, Ferdinand, Enk, Alexander, Deinlein, Teresa, Hofmann-Wellenhof, Rainer, Thomas, Luc, Lallas, Aimilios, Blum, Andreas, Stolz, Wilhelm et al. · 2019 · JAMA Dermatology
  18. Large-scale pancreatic cancer detection via non-contrast CT and deep learning
    Cao, Kai, Xia, Yingda, Yao, Jiawen, Han, Xu, Lambert, Lukas, Zhang, Tingting, Tang, Wei, Jin, Gang, Jiang, Hui, Fang, Xu et al. · 2023 · Nature Medicine
  19. Spectral-Domain OCT Measurements in Alzheimer’s Disease
    Victor T.T. Chan, Zihan Sun, Shumin Tang, Li Jia Chen, Adrian Wong, Clement Chee Yung Tham, Tien Yin Wong, Christopher Chen, M. Kamran Ikram, Heather Elizabeth Whitson et al. · 2018 · Ophthalmology
  20. Precision medicine in the era of artificial intelligence: implications in chronic disease management
    Subramanian, Murugan, Wojtusciszyn, Anne, Favre, Lucie, Boughorbel, Sabri, Shan, Jingxuan, Letaief, Khaled B., Pitteloud, Nelly, Chouchane, Lotfi · 2020 · Journal of Translational Medicine
  21. Towards multi-modal causability with graph neural networks enabling information fusion for explainable ai
    Holzinger, Andreas, Malle, Bernd, Saranti, Anna, Pfeifer, Bastian · 2021 · Information Fusion
  22. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT
    Julián Betancur, Frédéric Commandeur, M Esmaeili Motlagh, Tali Sharir, Andrew Jeffrey Einstein, Sabahat Bokhari, Mathews B. Fish, Terrence David Ruddy, Philipp Antonio Kaufmann, Albert J. Sinusas et al. · 2018 · JACC. Cardiovascular imaging
  23. Machine learning applications in epilepsy
    Bardia Abbasi, Daniel M. Goldenholz · 2019 · Epilepsia
  24. Distributed deep learning networks among institutions for medical imaging
    Chang, Ken, Balachandar, Niranjan, Lam, Carson, Yi, Darvin, Brown, James, Beers, Andrew, Rosen, Bruce, Rubin, Daniel L, Kalpathy-Cramer, Jayashree · 2018 · Journal of the American Medical Informatics Association
  25. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories
    Placido, Davide, Yuan, Bo, Hjaltelin, Jessica X., Zheng, Chunlei, Haue, Amalie D., Chmura, Piotr J., Yuan, Chen, Kim, Jihye, Umeton, Renato, Antell, Gregory et al. · 2023 · Nature Medicine
  26. What is AI? Applications of artificial intelligence to dermatology
    Du‐Harpur, X., Watt, F.M., Luscombe, N.M., Lynch, M.D. · 2020 · British Journal of Dermatology
  27. Superior skin cancer classification by the combination of human and artificial intelligence
    Achim Hekler, Jochen Sven Utikal, Alexander Heinrich Enk, Axel Hauschild, Michael Weichenthal, Roman C. Maron, Carola Berking, Sebastian Haferkamp, Joachim Klode, Dirk Schadendorf et al. · 2019 · European Journal of Cancer
  28. Multiscale Modeling Meets Machine Learning: What Can We Learn?
    Grace C. Y. Peng, Mark Alber, Adrián Buganza Tepole, William R. Cannon, Suvranu De, Salvador Durá-Bernal, Krishna Garikipati, George Em Karniadakis, William W. Lytton, Paris G. Perdikaris et al. · 2020 · Archives of Computational Methods in Engineering
  29. Breast cancer histopathological image classification using a hybrid deep neural network
    Rui Yan, Fei Ren, Zihao Wang, Lihua Wang, Tong Zhang, Yudong Liu, Xiaosong Rao, Chun-Hou Zheng, Fa Zhang · 2019 · Methods
  30. Social Media– and Internet-Based Disease Surveillance for Public Health
    Allison E. Aiello, Audrey Renson, Paul N. Zivich · 2020 · Annual Review of Public Health
  31. Causality matters in medical imaging
    Castro, Daniel C., Walker, Ian, Glocker, Ben · 2020 · Nature Communications
  32. Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
    Dimiduk, Dennis M., Holm, Elizabeth A., Niezgoda, Stephen R. · 2018 · Integrating Materials and Manufacturing Innovation
  33. DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs
    Yifan Peng, Shazia Dharssi, Qingyu Chen, Tiarnán D L Keenan, Elvira Agrón, Wai T. Wong, Emily Y. Chew, Zhiyong Lu · 2018 · Ophthalmology
  34. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images
    Zhiming Cui, Yu Fang, Lanzhuju Mei, Bojun Zhang, Bo Yu, Jiameng Liu, Caiwen Jiang, Yuhang Sun, Lei Ma, Jiawei Huang et al. · 2022 · Nature Communications
  35. Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks
    Ling‐Ping Cen, Jie Ji, Jianwei Lin, Si-Tong Ju, Hong-Jie Lin, Tai-Ping Li, Yun Wang, Jian-Feng Yang, Yu-Fen Liu, Shaoying Tan et al. · 2021 · Nature Communications
  36. Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning
    Mohammad Farukh Hashmi, Satyarth Katiyar, Avinash G. Keskar, Neeraj Dhanraj Bokde, Zong Woo Geem · 2020 · Diagnostics
  37. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis
    Chen, Po-Hsuan Cameron, Gadepalli, Krishna, MacDonald, Robert, Liu, Yun, Kadowaki, Shiro, Nagpal, Kunal, Kohlberger, Timo, Dean, Jeffrey, Corrado, Greg S., Hipp, Jason D. et al. · 2019 · Nature Medicine
  38. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)
    Fan, Wenjuan, Liu, Jingnan, Zhu, Shuwan, Pardalos, Panos M. · 2020 · Annals of Operations Research
  39. Rethinking the performance comparison between SNNS and ANNS
    Lei Deng, Yujie Wu, Xing Hu, Ling Liang, Yufei Ding, Guoqi Li, Guangshe Zhao, Peng Li, Yuan Xie · 2019 · Neural Networks
  40. The role of artificial intelligence in precision medicine
    Mesko, Bertalan · 2017 · Expert Review of Precision Medicine and Drug Development
  41. Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach
    Zhang, Bo, Shi, Huiping, Wang, Hongtao · 2023 · Journal of Multidisciplinary Healthcare
  42. Machine Learning and the Future of Cardiovascular Care
    Giorgio Quer, Ramy Arnaout, Michael Henne, Rima Arnaout · 2021 · Journal of the American College of Cardiology
  43. Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss
    Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng‐Ann Heng · 2018 · International Joint Conference on Artificial Intelligence
  44. Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging
    Arun, Nishanth, Gaw, Nathan, Singh, Praveer, Chang, Ken, Aggarwal, Mehak, Chen, Bryan, Hoebel, Katharina, Gupta, Sharut, Patel, Jay, Gidwani, Mishka et al. · 2021 · Radiology: Artificial Intelligence
  45. Understanding, explaining, and utilizing medical artificial intelligence
    Cadario, Romain, Longoni, Chiara, Morewedge, Carey K. · 2021 · Nature Human Behaviour
  46. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans
    Kaan Orhan, İbrahim Şevki Bayrakdar, Matvey Ezhov, Alexander Kravtsov, Taha Özyürek · 2020 · International Endodontic Journal
  47. Attention Gate ResU-Net for Automatic MRI Brain Tumor Segmentation
    Jianxin Zhang, Zongkang Jiang, Jing Dong, Yaqing Hou, Bin Liu · 2020 · IEEE Access
  48. Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images
    Khosravi, Pegah, Kazemi, Ehsan, Imielinski, Marcin, Elemento, Olivier, Hajirasouliha, Iman · 2018 · EBioMedicine
  49. Radimagenet: an open Radiologic deep learning research Dataset for effective transfer learning
    Mei, Xueyan, Liu, Zelong, Robson, Philip M., Marinelli, Brett, Huang, Mingqian, Doshi, Amish, Jacobi, Adam, Cao, Chendi, Link, Katherine E., Yang, Thomas et al. · 2022 · Radiology Artificial Intelligence
  50. Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
    Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur, Nico Karssemeijer, Elena Marchiori, Mehran Pesteie, Charles R.G. Guttmann, Frank‐Erik de Leeuw, Clare M. Tempany, Bram van Ginneken et al. · 2017 · Lecture notes in computer science
  51. From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities
    Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Anastasia Oikonomou, Habib Benali · 2019 · IEEE Signal Processing Magazine
  52. Models Genesis
    Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang, Michael B. Gotway, Jianming Liang · 2020 · Medical Image Analysis
  53. Device Placement Optimization with Reinforcement Learning
    Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, J. Dean · 2017 · International Conference on Machine Learning
  54. A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability
    Saad Mahmud Khan, Xiaoxuan Liu, Siddharth Nath, Edward Korot, Livia Faes, Siegfried K. Wagner, Pearse Andrew Keane, Neil James Sebire, Matthew J. Burton, Alastair Keith Denniston · 2020 · The Lancet Digital Health
  55. The Role of Artificial Intelligence in Early Cancer Diagnosis
    Benjamin Hunter, Sumeet Hindocha, Richard W. Lee · 2022 · Cancers
  56. Ethical dimensions of using artificial intelligence in health care
    2019 · AMA Journal of Ethics
  57. Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks
    Philipp Tschandl, Cliff O. Rosendahl, Bengü Nisa Akay, Giuseppe Argenziano, Andreas Blum, Ralph Peter Braun, Horacio Cabo, Jean-Yves Gourhant, Jürgen F. Kreusch, Aimilios Lallas et al. · 2018 · JAMA Dermatology
  58. Impact of a deep learning assistant on the histopathologic classification of liver cancer
    Kiani, Amirhossein, Uyumazturk, Bora, Rajpurkar, Pranav, Wang, Alex, Gao, Rebecca, Jones, Erik, Yu, Yifan, Langlotz, Curtis P., Ball, Robyn L., Montine, Thomas J. et al. · 2020 · npj Digital Medicine
  59. Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images
    Michael Armando Marchetti, Noel Codella, Stephen W. Dusza, David A. Gutman, Brian Helba, Aadi Kalloo, Nabin K. Mishra, Cristina Carrera, M. Emre Celebi, Jennifer DeFazio et al. · 2017 · Journal of the American Academy of Dermatology
  60. Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images
    Imran Iqbal, Muhammad Younus, Khuram Walayat, Mohib Ullah Kakar, Jinwen Ma · 2020 · Computerized Medical Imaging and Graphics
  61. Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
    Marc Aubreville, Christian Knipfer, Nicolai Oetter, Christian Jaremenko, Erik Rodner, Joachim Denzler, Christopher Bohr, Helmut Neumann, Florian Stelzle, Andreas Maier · 2017 · Scientific Reports
  62. Multimodal skin lesion classification using deep learning
    Yap, Jordan, Yolland, William, Tschandl, Philipp · 2018 · Experimental Dermatology
  63. On the Robustness of Semantic Segmentation Models to Adversarial Attacks
    Anurag Arnab, Ondřej Mikšík, Philip H. S. Torr · 2018 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  64. Detecting caries lesions of different radiographic extension on bitewings using deep learning
    Anselmo García Cantú Ros, Sascha Gehrung, Joachim Krois, Akhilanand Chaurasia, Jesús Gómez Rossi, Robert Gaudin, Karim Elhennawy, Falk Schwendicke · 2020 · Journal of Dentistry
  65. Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
    Yiting Li, Haisong Huang, Qingsheng Xie, Liguo Yao, Qipeng Chen · 2018 · Applied Sciences
  66. Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
    Liu, Hanruo, Li, Liu, Wormstone, I. Michael, Qiao, Chunyan, Zhang, Chun, Liu, Ping, Li, Shuning, Wang, Huaizhou, Mou, Dapeng, Pang, Ruiqi et al. · 2019 · JAMA Ophthalmology
  67. Predicting Infectious Disease Using Deep Learning and Big Data
    Sangwon Chae, Sungjun Kwon, Dong-Hyun Lee · 2018 · International Journal of Environmental Research and Public Health
  68. Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale
    Kan Yao, Rohit Unni, Yuebing Zheng · 2018 · Nanophotonics
  69. Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art
    Adekanmi Adeyinka Adegun, Serestina Viriri · 2020 · Artificial Intelligence Review
  70. A Review of Challenges and Opportunities in Machine Learning for Health
    Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Irene Y. Chen, Rajesh Ranganath · 2018 · PubMed Central
  71. Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model
    Zhen Ding, Huiying Shi, Hao Zhang, Lingjun Meng, Mengke Fan, Chaoqun Han, Kun Zhang, Fanhua Ming, Xiaoping Xie, Hao Liu et al. · 2019 · Gastroenterology
  72. Designing deep learning studies in cancer diagnostics
    Andreas Kleppe, Ole-Johan Skrede, Sepp De Raedt, Knut Liestøl, David James Kerr, Havard Emil Danielsen · 2021 · Nature reviews. Cancer
  73. Deep‐learning‐based, computer‐aided classifier developed with a small dataset of clinical images surpasses board‐certified dermatologists in skin tumour diagnosis
    Yasuhiro Fujisawa, Yasuhiro Otomo, Y. Ogata, Yoshiyuki Nakamura, R. Fujita, Yosuke Ishitsuka, Ryo Watanabe, Naoko Okiyama, K. Ohara, Manabu Fujimoto · 2018 · British Journal of Dermatology
  74. Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning
    Yang Yang, Lin‐Feng Yan, Xin Zhang, Yu Han, Hai‐Yan Nan, Yu‐Chuan Hu, Bo Hu, Songlin Yan, Jin Zhang, Dong-Liang Cheng et al. · 2018 · Frontiers in Neuroscience
  75. Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning
    Arnaout, Rima, Curran, Lara, Zhao, Yili, Levine, Jami C., Chinn, Erin, Moon-Grady, Anita J. · 2020
  76. Update on Keratinocyte Carcinomas
    Kishwer S. Nehal, Christopher Keram Bichakjian · 2018 · New England Journal of Medicine
  77. Machine Learning for Surgical Phase Recognition
    Carly R. Garrow, Karl‐Friedrich Kowalewski, Linhong Li, Martin Wagner, Mona Wanda Schmidt, Sandy Engelhardt, Daniel A. Hashimoto, Hannes Kenngott, Sebastian Bodenstedt, Stefanie Speidel et al. · 2020 · Annals of Surgery
  78. Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods
    Mücahid Barstuğan, Umut Özkaya, Şaban Öztürk · 2020 · arXiv (Cornell University)
  79. Deep learning algorithm predicts diabetic retinopathy progression in individual patients
    Arcadu, Filippo, Benmansour, Fethallah, Maunz, Andreas, Willis, Jeff, Haskova, Zdenka, Prunotto, Marco · 2019 · npj Digital Medicine
  80. Pathologist-level interpretable whole-slide cancer diagnosis with deep learning
    Zhang, Zizhao, Chen, Pingjun, McGough, Mason, Xing, Fuyong, Wang, Chunbao, Bui, Marilyn, Xie, Yuanpu, Sapkota, Manish, Cui, Lei, Dhillon, Jasreman et al. · 2019 · Nature Machine Intelligence
  81. Automated identification and grading system of diabetic retinopathy using deep neural networks
    Wei Zhang, Jie Zhong, Shijun Yang, Zhentao Gao, Junjie Hu, Yuanyuan Chen, Yi Zhang · 2019 · Knowledge-Based Systems
  82. Skin cancer diagnosis based on optimized convolutional neural network
    Ni Zhang, Yi-Xin Cai, Yong-Yong Wang, Yitao Tian, Xiaoli Wang, Benjamin Badami · 2019 · Artificial Intelligence in Medicine
  83. Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification
    Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Georg Dorffner, Rupert Ecker, Isabella Ellinger · 2020 · Computer Methods and Programs in Biomedicine
  84. Literature review: efficient deep neural networks techniques for medical image analysis
    Mohamed A. Abdou · 2022 · Neural Computing and Applications
  85. Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning
    Liyue Shen, Wei Zhao, Lei Xing · 2019 · Nature Biomedical Engineering
  86. Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India
    Gulshan, Varun, Rajan, Renu P., Widner, Kasumi, Wu, Derek, Wubbels, Peter, Rhodes, Tyler, Whitehouse, Kira, Coram, Marc, Corrado, Greg, Ramasamy, Kim et al. · 2019 · JAMA Ophthalmology
  87. DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks
    Chen, Huili, Fu, Cheng, Zhao, Jishen, Koushanfar, Farinaz · 2019 · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
  88. Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing
    Boecking, Benedikt, Usuyama, Naoto, Bannur, Shruthi, Castro, Daniel C., Schwaighofer, Anton, Hyland, Stephanie, Wetscherek, Maria, Naumann, Tristan, Nori, Aditya, Alvarez-Valle, Javier et al. · 2022 · Lecture Notes in Computer Science
  89. A survey on incorporating domain knowledge into deep learning for medical image analysis
    Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu · 2021 · Medical Image Analysis
  90. Bad News? Send an AI. Good News? Send a Human
    Aaron M. Garvey, TaeWoo Kim, Adam Duhachek · 2021 · Journal of Marketing
  91. Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs
    Chi‐Tung Cheng, Tsung‐Ying Ho, Tao-Yi Lee, Chih-Chen Chang, Ching-Cheng Chou, Chih-Chi Chen, I‐Fang Chung, Chien‐Hung Liao · 2019 · European Radiology
  92. Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI
    Andreas Fügener, Jörn Grahl, Alok Gupta, Wolfgang Ketter · 2021 · MIS Quarterly
  93. Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss
    Pingjun Chen, Linlin Gao, Xiaoshuang Shi, Kyle D. Allen, Lin Yang · 2019 · Computerized Medical Imaging and Graphics
  94. A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
    Liu Li, Mai Xu, Hanruo Liu, Yang Li, Xiaofei Wang, Lai Wei Jiang, Zulin Wang, Xiang Fan, Ningli Wang · 2019 · IEEE Transactions on Medical Imaging
  95. Survey on deep learning for radiotherapy
    Meyer, Philippe, Noblet, Vincent, Mazzara, Christophe, Lallement, Alex · 2018 · Computers in Biology and Medicine
  96. Predicting conversion to wet age-related macular degeneration using deep learning
    Yim, Jason, Chopra, Reena, Spitz, Terry, Winkens, Jim, Obika, Annette, Kelly, Christopher, Askham, Harry, Lukic, Marko, Huemer, Josef, Fasler, Katrin et al. · 2020 · Nature Medicine
  97. Skin Lesion Classification Using Hybrid Deep Neural Networks
    Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Isabella Ellinger · 2019 · IEEE International Conference on Acoustics Speech and Signal Processing
  98. Deep Learning Based Analysis of Histopathological Images of Breast Cancer
    Juanying Xie, Ran Liu, Joseph Luttrell, Chaoyang Zhang · 2019 · Frontiers in Genetics
  99. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task
    Titus Josef Brinker, Achim Hekler, Alexander Heinrich Enk, Joachim Klode, Axel Hauschild, Carola Berking, Bastian Schilling, Sebastian Haferkamp, Dirk Schadendorf, Stefan Fröhling et al. · 2019 · European Journal of Cancer
  100. Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks
    Annarumma, Mauro, Withey, Samuel J., Bakewell, Robert J., Pesce, Emanuele, Goh, Vicky, Montana, Giovanni · 2019 · Radiology