Dermatologist-level classification of skin cancer with deep neural networks

Cited by (1,225)

Items 1,001–1,100 of 1,225

1. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
   Joo, Sunghoon, Ko, Eun Sook, Kwon, Soonhwan, Jeon, Eunjoo, Jung, Hyungsik, Kim, Ji-Yeon, Chung, Myung Jin, Im, Young-Hyuck · 2021 · Scientific Reports
2. Recommendations and future directions for supervised machine learning in psychiatry
   Cearns, Micah, Hahn, Tim, Baune, Bernhard T. · 2019 · Translational Psychiatry
3. Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
   Fortelny, Nikolaus, Bock, Christoph · 2020 · Genome Biology
4. Exploring behavioural intentions toward smart healthcare services among medical practitioners: a technology transfer perspective
   Pan, Jinxin, Ding, Shuai, Wu, Desheng, Yang, Shanlin, Yang, Jun · 2019 · International Journal of Production Research
5. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review
   Zhou, Qian, Chen, Zhi-hang, Cao, Yi-heng, Peng, Sui · 2021 · npj Digital Medicine
6. The Future of Precision Medicine in the Cure of Alzheimer’s Disease
   Arafah, Azher, Khatoon, Saima, Rasool, Iyman, Khan, Andleeb, Rather, Mashoque Ahmad, Abujabal, Khaled Abdullah, Faqih, Yazid Abdullilah Hassan, Rashid, Hina, Rashid, Shahzada Mudasir, Bilal Ahmad, Sheikh et al. · 2023 · Biomedicines
7. Clinical applications of artificial intelligence in sepsis: A narrative review
   Schinkel, M., Paranjape, K., Nannan Panday, R.S., Skyttberg, N., Nanayakkara, P.W.B. · 2019 · Computers in Biology and Medicine
8. A novel vision transformer model for skin cancer classification
   Yang, Guang, Luo, Suhuai, Greer, Peter · 2023 · Neural Processing Letters
9. Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics
   Pavuluri, Suresh, Sangal, Rohit, Sather, John, Taylor, R Andrew · 2024 · BMJ Health & Care Informatics
10. Human and artificial cognition
    Siemens, George, Marmolejo-Ramos, Fernando, Gabriel, Florence, Medeiros, Kelsey, Marrone, Rebecca, Joksimovic, Srecko, de Laat, Maarten · 2022 · Computers and Education: Artificial Intelligence
11. Automated Dermatological Diagnosis: Hype or Reality?
    Navarrete-Dechent, Cristian, Dusza, Stephen W., Liopyris, Konstantinos, Marghoob, Ashfaq A., Halpern, Allan C., Marchetti, Michael A. · 2018 · Journal of Investigative Dermatology
12. Deep learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images
    Cheng, Na, Ren, Yong, Zhou, Jing, Zhang, Yiwang, Wang, Deyu, Zhang, Xiaofang, Chen, Bing, Liu, Fang, Lv, Jin, Cao, Qinghua et al. · 2022 · Gastroenterology
13. Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches
    Weng, Stephen F., Vaz, Luis, Qureshi, Nadeem, Kai, Joe · 2019 · PLOS ONE
14. Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices
    Weidener, Lukas, Fischer, Michael · 2023 · Perspectives on Medical Education
15. Evaluating Large Language Models in Theory of Mind Tasks
    Kosinski, Michal · 2023 · arXiv
16. Melanoma Lesion Detection and Segmentation Using YOLOv4-DarkNet and Active Contour
    Albahli, Saleh, Nida, Nudrat, Irtaza, Aun, Yousaf, Muhammad Haroon, Mahmood, Muhammad Tariq · 2020 · IEEE Access
17. Fast and scalable search of whole-slide images via self-supervised deep learning
    Chen, Chengkuan, Lu, Ming Y., Williamson, Drew F. K., Chen, Tiffany Y., Schaumberg, Andrew J., Mahmood, Faisal · 2022 · Nature Biomedical Engineering
18. Artificial intelligence and primary care research: a scoping review
    Kueper, Jacqueline K., Terry, Amanda L., Zwarenstein, Merrick, Lizotte, Daniel J. · 2020 · The Annals of Family Medicine
19. Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey
    Maassen, Oliver, Fritsch, Sebastian, Palm, Julia, Deffge, Saskia, Kunze, Julian, Marx, Gernot, Riedel, Morris, Schuppert, Andreas, Bickenbach, Johannes · 2021 · Journal of Medical Internet Research
20. Governing the safety of artificial intelligence in healthcare
    Macrae, Carl · 2019 · BMJ Quality & Safety
21. Algorithms on regulatory lockdown in medicine
    Babic, Boris, Gerke, Sara, Evgeniou, Theodoros, Cohen, I. Glenn · 2019 · Science
22. Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks
    Lu, Zhuoran, Yin, Ming · 2021 · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
23. Uses and Limitations of Artificial Intelligence for Oncology
    Kolla, Likhitha, Parikh, Ravi B. · 2024 · Cancer
24. Trust in artificial intelligence for medical diagnoses
    2020 · Progress in Brain Research
25. Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology
    Olsen, Thomas George, Jackson, B. Hunter, Feeser, Theresa Ann, Kent, Michael N., Moad, John C., Krishnamurthy, Smita, Lunsford, Denise D., Soans, Rajath E. · 2018 · Journal of Pathology Informatics
26. Deep Learning: A Bayesian Perspective
    Polson, Nicholas G., Sokolov, Vadim · 2017 · Bayesian Analysis
27. Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics
    Jiang, Yuming, Zhou, Kangneng, Sun, Zepang, Wang, Hongyu, Xie, Jingjing, Zhang, Taojun, Sang, Shengtian, Islam, Md Tauhidul, Wang, Jen-Yeu, Chen, Chuanli et al. · 2023 · Cell Reports Medicine
28. Deep learning in biomedical data science
    Baldi, Pierre · 2018 · Annual Review of Biomedical Data Science
29. Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners
    Kocaballi, A Baki, Ijaz, Kiran, Laranjo, Liliana, Quiroz, Juan C, Rezazadegan, Dana, Tong, Huong Ly, Willcock, Simon, Berkovsky, Shlomo, Coiera, Enrico · 2020 · Journal of the American Medical Informatics Association
30. Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics
    Warnat-Herresthal, Stefanie, Perrakis, Konstantinos, Taschler, Bernd, Becker, Matthias, Baßler, Kevin, Beyer, Marc, Günther, Patrick, Schulte-Schrepping, Jonas, Seep, Lea, Klee, Kathrin et al. · 2020 · iScience
31. A deep 3D residual<scp>CNN</scp>for false‐positive reduction in pulmonary nodule detection
    Jin, Hongsheng, Li, Zongyao, Tong, Ruofeng, Lin, Lanfen · 2018 · Medical Physics
32. Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets
    Tasci, Erdal, Zhuge, Ying, Camphausen, Kevin, Krauze, Andra V. · 2022 · Cancers
33. Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
    Perreault Levasseur, Laurence, Hezaveh, Yashar D., Wechsler, Risa H. · 2017 · The Astrophysical Journal Letters
34. Learning to Complement Humans
    Wilder, Bryan, Horvitz, Eric, Kamar, Ece · 2020 · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
35. Fusion of deep learning models of MRI scans, Mini–Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment
    Qiu, Shangran, Chang, Gary H., Panagia, Marcello, Gopal, Deepa M., Au, Rhoda, Kolachalama, Vijaya B. · 2018 · Alzheimer's &amp; Dementia: Diagnosis, Assessment &amp; Disease Monitoring
36. Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning
    Looney, Pádraig, Stevenson, Gordon N., Nicolaides, Kypros H., Plasencia, Walter, Molloholli, Malid, Natsis, Stavros, Collins, Sally L. · 2018 · JCI Insight
37. Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification
    Höhn, Julia, Krieghoff-Henning, Eva, Jutzi, Tanja B., von Kalle, Christof, Utikal, Jochen S., Meier, Friedegund, Gellrich, Frank F., Hobelsberger, Sarah, Hauschild, Axel, Schlager, Justin G. et al. · 2021 · European Journal of Cancer
38. Artificial intelligence for breast cancer screening: Opportunity or hype?
    Houssami, Nehmat, Lee, Christoph I., Buist, Diana S.M., Tao, Dacheng · 2017 · The Breast
39. Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
    Lu, Lin, Dercle, Laurent, Zhao, Binsheng, Schwartz, Lawrence H. · 2021 · Nature Communications
40. Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap
    Sokol, Kacper, Fackler, James, Vogt, Julia E. · 2025 · npj Digital Medicine
41. Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network
    Roffman, David, Hart, Gregory, Girardi, Michael, Ko, Christine J., Deng, Jun · 2018 · Scientific Reports
42. Synthetic Examples Improve Generalization for Rare Classes
    Beery, Sara, Liu, Yang, Morris, Dan, Piavis, Jim, Kapoor, Ashish, Meister, Markus, Joshi, Neel, Perona, Pietro · 2020 · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
43. Systematic Review of Deep Learning Image Analyses for the Diagnosis and Monitoring of Skin Disease
    Choy, Shern Ping, Kim, Byung Jin, Paolino, Alexandra, Tan, Wei Ren, Lim, Sarah Man Lin, Seo, Jessica, Tan, Sze Ping, Francis, Luc, Tsakok, Teresa, Simpson, Michael et al. · 2023 · npj Digital Medicine
44. Deep Deterministic Uncertainty: A New Simple Baseline
    Mukhoti, Jishnu, Kirsch, Andreas, van Amersfoort, Joost, Torr, Philip H.S., Gal, Yarin · 2023 · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
45. A Comparison of Transfer Learning Performance Versus Health Experts in Disease Diagnosis from Medical Imaging
    Malik, Hassaan, Farooq, Muhammad Shoaib, Khelifi, Adel, Abid, Adnan, Nasir Qureshi, Junaid, Hussain, Muzammil · 2020 · IEEE Access
46. AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function
    Pham, Tri-Cong, Luong, Chi-Mai, Hoang, Van-Dung, Doucet, Antoine · 2021 · Scientific Reports
47. Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis
    Natekar, Parth, Kori, Avinash, Krishnamurthi, Ganapathy · 2020 · Frontiers in Computational Neuroscience
48. AI-driven business analytics and decision making
    Oluwaseun Badmus, Shahab Anas Rajput, John Babatope Arogundade, Mosope Williams · 2024 · World Journal of Advanced Research and Reviews
49. Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&amp;E images
    Rawat, Rishi R., Ortega, Itzel, Roy, Preeyam, Sha, Fei, Shibata, Darryl, Ruderman, Daniel, Agus, David B. · 2020 · Scientific Reports
50. Artificial intelligence enabled automated diagnosis and grading of ulcerative colitis endoscopy images
    Sutton, Reed T., Zai͏̈ane, Osmar R., Goebel, Randolph, Baumgart, Daniel C. · 2022 · Scientific Reports
51. Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency
    Anik, Ariful Islam, Bunt, Andrea · 2021 · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
52. Convolutional Neural Network Classifies Pathological Voice Change in Laryngeal Cancer with High Accuracy
    Kim, HyunBum, Jeon, Juhyeong, Han, Yeon Jae, Joo, YoungHoon, Lee, Jonghwan, Lee, Seungchul, Im, Sun · 2020 · Journal of Clinical Medicine
53. Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
    Deng, Wesley Hanwen, Nagireddy, Manish, Lee, Michelle Seng Ah, Singh, Jatinder, Wu, Zhiwei Steven, Holstein, Kenneth, Zhu, Haiyi · 2022 · 2022 ACM Conference on Fairness Accountability and Transparency
54. Current challenges of digital health interventions in Pakistan: mixed methods analysis
    Kazi, Abdul Momin, Qazi, Saad Ahmed, Ahsan, Nazia, Khawaja, Sadori, Sameen, Fareeha, Saqib, Muhammad, Khan Mughal, Muhammad Ayub, Wajidali, Zabin, Ali, Sikander, Ahmed, Rao Moueed et al. · 2020 · Journal of Medical Internet Research
55. Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms
    Udrea, A., Mitra, G.D., Costea, D., Noels, E.C., Wakkee, M., Siegel, D.M., de Carvalho, T.M., Nijsten, T.E.C. · 2020 · Journal of the European Academy of Dermatology and Venereology
56. Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
    Ho, Thi Kieu, Gwak, Jeonghwan · 2019 · Applied Sciences
57. Deep Learning to Estimate Biological Age From Chest Radiographs
    Raghu, Vineet K., Weiss, Jakob, Hoffmann, Udo, Aerts, Hugo J.W.L., Lu, Michael T. · 2021 · JACC: Cardiovascular Imaging
58. Artificial intelligence in dermatopathology: diagnosis, education, and research
    Wells, Amy, Patel, Shaan, Lee, Jason B., Motaparthi, Kiran · 2021 · Journal of Cutaneous Pathology
59. Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce
    Mazurowski, Maciej A. · 2019 · Journal of the American College of Radiology
60. Patient perspectives on informed consent for medical AI: A web-based experiment
    Park, Hai Jin · 2024 · DIGITAL HEALTH
61. Classifying vortex wakes using neural networks
    Colvert, Brendan, Alsalman, Mohamad, Kanso, Eva · 2018 · Bioinspiration &amp; Biomimetics
62. Teledermatology and its current perspective
    Pasquali, Paola, Sonthalia, Sidharth, Moreno-Ramirez, David, Sharma, Pooram, Agrawal, Mahima, Gupta, Somesh, Kumar, Dinesh, Arora, Dharmendra · 2020 · Indian Dermatology Online Journal
63. DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification
    Yang, Yijun, Fu, Huazhu, Aviles-Rivero, Angelica I., Schönlieb, Carola-Bibiane, Zhu, Lei · 2023 · Lecture Notes in Computer Science
64. <i>BinFI</i>: an efficient fault injector for safety-critical machine learning systems
    Chen, Zitao, Li, Guanpeng, Pattabiraman, Karthik, DeBardeleben, Nathan · 2019 · Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
65. Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction
    Kwon, Joon-myoung, Jeon, Ki-Hyun, Kim, Hyue Mee, Kim, Min Jeong, Lim, Sungmin, Kim, Kyung-Hee, Song, Pil Sang, Park, Jinsik, Choi, Rak Kyeong, Oh, Byung-Hee · 2019 · PLOS ONE
66. The EMory BrEast imaging Dataset (EMBED): a racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images
    Jeong, Jiwoong J., Vey, Brianna L., Bhimireddy, Ananth, Kim, Thomas, Santos, Thiago, Correa, Ramon, Dutt, Raman, Mosunjac, Marina, Oprea-Ilies, Gabriela, Smith, Geoffrey et al. · 2023 · Radiology Artificial Intelligence
67. Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography
    Alves, Natália, Schuurmans, Megan, Litjens, Geke, Bosma, Joeran S., Hermans, John, Huisman, Henkjan · 2022 · Cancers
68. Enhancing Triage Efficiency and Accuracy in Emergency Rooms for Patients with Metastatic Prostate Cancer: A Retrospective Analysis of Artificial Intelligence-Assisted Triage Using ChatGPT 4.0
    Gebrael, Georges, Sahu, Kamal Kant, Chigarira, Beverly, Tripathi, Nishita, Mathew Thomas, Vinay, Sayegh, Nicolas, Maughan, Benjamin L., Agarwal, Neeraj, Swami, Umang, Li, Haoran · 2023 · Cancers
69. Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education
    Weidener, Lukas, Fischer, Michael · 2024 · JMIR Medical Education
70. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening
    Liu, Huanhuan, Chen, Yanhong, Zhang, Yuzhen, Wang, Lijun, Luo, Ran, Wu, Haoting, Wu, Chenqing, Zhang, Huiling, Tan, Weixiong, Yin, Hongkun et al. · 2021 · European Radiology
71. Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
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72. FairLens: Auditing black-box clinical decision support systems
    Panigutti, Cecilia, Perotti, Alan, Panisson, André, Bajardi, Paolo, Pedreschi, Dino · 2021 · Information Processing &amp; Management
73. Enhancing histopathological medical image classification for Early cancer diagnosis using deep learning and explainable AI – LIME &amp; SHAP
    Ukwuoma, Chiagoziem C., Cai, Dongsheng, Eziefuna, Ebere O., Oluwasanmi, Ariyo, Abdi, Sabirin F., Muoka, Gladys W., Thomas, Dara, Sarpong, Kwabena · 2025 · Biomedical Signal Processing and Control
74. Biological insights through omics data integration
    Noor, Elad, Cherkaoui, Sarah, Sauer, Uwe · 2019 · Current Opinion in Systems Biology
75. Explaining Machine Learning Models for Clinical Gait Analysis
    Slijepcevic, Djordje, Horst, Fabian, Lapuschkin, Sebastian, Horsak, Brian, Raberger, Anna-Maria, Kranzl, Andreas, Samek, Wojciech, Breiteneder, Christian, Schöllhorn, Wolfgang Immanuel, Zeppelzauer, Matthias · 2022 · ACM Transactions on Computing for Healthcare
76. Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation
    Puyol-Antón, Esther, Ruijsink, Bram, Mariscal Harana, Jorge, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Razavi, Reza, Chowienczyk, Phil, King, Andrew P. · 2022 · Frontiers in Cardiovascular Medicine
77. Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making
    Lee, Min Hun, Chew, Chong Jun · 2023 · Proceedings of the ACM on Human-Computer Interaction
78. Large language models and multimodal foundation models for precision oncology
    Truhn, Daniel, Eckardt, Jan-Niklas, Ferber, Dyke, Kather, Jakob Nikolas · 2024 · npj Precision Oncology
79. An end-to-end model based on improved adaptive deep belief network and its application to bearing fault diagnosis
    Xie, Jiaqi, Du, Guifu, Shen, Changqing, Chen, Nan, Chen, Liang, Zhu, Zhongkui · 2018 · IEEE Access
80. Can we trust AI not to further embed racial bias and prejudice?
    Noor, Poppy · 2020 · BMJ
81. The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence
    Cabitza, Federico, Zeitoun, Jean-David · 2019 · Annals of Translational Medicine
82. Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions
    Chen, Shanquan, Yu, Jiazhou, Chamouni, Sarah, Wang, Yuqi, Li, Yunfei · 2024 · BMC Medicine
83. Uncertainty-aware deep learning in healthcare: A scoping review
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84. AI in healthcare: A narrative review
    Väänänen, Antti, Haataja, Keijo, Vehviläinen-Julkunen, Katri, Toivanen, Pekka · 2021 · F1000Research
85. Medical Professional Enhancement Using Explainable Artificial Intelligence in Fetal Cardiac Ultrasound Screening
    Sakai, Akira, Komatsu, Masaaki, Komatsu, Reina, Matsuoka, Ryu, Yasutomi, Suguru, Dozen, Ai, Shozu, Kanto, Arakaki, Tatsuya, Machino, Hidenori, Asada, Ken et al. · 2022 · Biomedicines
86. Machine Learning and Imaging Informatics in Oncology
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87. Beyond Post hoc Explanations: A Comprehensive Framework for Accountable AI in Medical Imaging Through Transparency, Interpretability, and Explainability
    Singh, Yashbir, Hathaway, Quincy A., Keishing, Varekan, Salehi, Sara, Wei, Yujia, Horvat, Natally, Vera-Garcia, Diana V., Choudhary, Ashok, Mula Kh, Almurtadha, Quaia, Emilio et al. · 2025 · Bioengineering
88. Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study
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89. The Challenges of Regulating Artificial Intelligence in Healthcare Comment on "Clinical Decision Support and New Regulatory Frameworks for Medical Devices: Are We Ready for It? - A Viewpoint Paper"
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90. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
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91. Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging
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92. Enhanced Cyber Attack Detection Process for Internet of Health Things (IoHT) Devices Using Deep Neural Network
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93. Factors influencing clinicians' willingness to use an AI-based clinical decision support system
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94. AI support for ethical decision-making around resuscitation: proceed with care
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95. Physical Therapists’ Knowledge and Attitudes Regarding Artificial Intelligence Applications in Health Care and Rehabilitation: Cross-sectional Study
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96. Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis
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97. Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
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98. A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions
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99. It is Not “Accuracy vs. Explainability”—We Need Both for Trustworthy AI Systems
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100. Experts or Authorities? The Strange Case of the Presumed Epistemic Superiority of Artificial Intelligence Systems
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