Citations: Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://t.co/85C9iXUs4r Rajpurkar, P., et al. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning.
Dermatologist-level classification of skin cancer with deep neural networks.
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Sep 2018 – Mar 2025Where it's discussed
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What people are saying
The discussions extensively praise the groundbreaking nature of the study, with phrases like 'game-changing' and comparisons to experts, which reflect enthusiasm and recognition of its significance; the tone is optimistic and highly engaged, often referencing real-world implications and detailed technical aspects.
Most discussions praise the research, emphasizing its success in matching or surpassing dermatologists, which shows strong support for the findings.
Posts express high curiosity and excitement, especially regarding AI's capabilities and implications for healthcare.
Multiple discussions delve into the methodology, potential applications, and future impacts, indicating deep involvement.
Several comments highlight its transformative potential in medical diagnostics and global health access, indicating significant influence.
Abstract in brief
- The study demonstrates that a deep convolutional neural network (CNN) can classify skin cancer accurately using just images and disease labels, showing performance comparable to expert dermatologists on two critical skin cancer detection tasks.
- The CNN was trained on a very large dataset of over 129,450 clinical images, which is much bigger than previous datasets, allowing it to handle the fine-grained differences in skin lesion appearance.
- This technology has the potential to expand dermatological care globally by being integrated into mobile devices, enabling low-cost, accessible skin cancer screening outside clinics, especially given the widespread use of smartphones.]
Top posts
Most liked on X and BlueskyIn 2017 a paper from Stanford titled "Dermatologist-level classification of skin cancer with deep neural networks" was published in Nature. 8 years later I paid 130€ to a dermatologist to check for skin cancer. Someone should take care of this...
Dermatologist-level classification of skin cancer with deep neural networks https://t.co/9e53FlpnM5 #ccme2021 #ai #artificialintelligence
Dermatologist-level classification of skin cancer with deep neural networks - game-changing paper quoted at #RCPIAC19 https://t.co/29y80jFJJD
Just read the paper by @SebastianThrun and team on Dermatologist-level classification of skin cancer with deep neural networks, the fact that the neural net outperforms some of the best doctors in the world just blows my mind. #DeepLearning
Research Club Alert and FREE BREAKFAST!! join us this Tuesday, October 29th at 9AM in the BME Library (MR5 2019). Alex Mathew will be presenting "Dermatologist-level classification of skin cancer with deep neural networks." #MachineLearning #UVA #DataScience
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Explainable AI and Responsible Machine Learning in Health Informatics
My research and teaching focus on artificial intelligence and machine learning for health informatics, emphasizing explainable and interpretable AI to ensure ethical and responsible decisions in healthcare. This work…
Advances in Digital Health Technology and Skin Cancer Detection
In 2017 a paper from Stanford titled "Dermatologist-level classification of skin cancer with deep neural networks" was published in Nature. 8 years later I paid 130€ to a dermatologist to check for skin cancer. Someone should take care of this...
Citations: Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://t.co/85C9iXUs4r Rajpurkar, P., et al. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning.
Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. 5. Relling MV, et al. Clinical implementation of pharmacogenomics: Overcoming genetic exceptionalism. Lancet Oncol. 2019;20(8):e420-e429. 6. Valdes AM, et al.
Dermatologist-level classification of skin cancer with deep neural networks | Nature https://t.co/cK2yn2QBls
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://t.co/U0vubtGDGv
RT @keefer007: Dermatologist-level classification of skin cancer with deep neural networks https://t.co/9e53FlpnM5 #ccme2021 #ai #artificia…
RT @keefer007: Dermatologist-level classification of skin cancer with deep neural networks https://t.co/9e53FlpnM5 #ccme2021 #ai #artificia…
RT @keefer007: Dermatologist-level classification of skin cancer with deep neural networks https://t.co/9e53FlpnM5 #ccme2021 #ai #artificia…
Dermatologist-level classification of skin cancer with deep neural networks https://t.co/9e53FlpnM5 #ccme2021 #ai #artificialintelligence
Dermatologist-level classification of skin cancer with deep neural networks https://t.co/p1MSJlRzL3
@docprimum @Izno91 @eelte747 @topcao Pour les nævus, l’article compare dermatologues et IA avec comme étalon la biopsie (ce qui est bien). La question est de savoir s’il faut aller à la biopsie ou non. L’IA semble faire au moins aussi bien, peut-être un chouïa mieux. https://t.co/CL6Mbg7y8e
Dermatologist-level classification of skin cancer with deep neural networks https://t.co/V2hl0iJ2Mx #AI #ML #DeepLearning #DataScience #cancer
RT @uva_biodatasci: Research Club Alert and FREE BREAKFAST!! join us this Tuesday, October 29th at 9AM in the BME Library (MR5 2019). Alex…
RT @uva_biodatasci: Research Club Alert and FREE BREAKFAST!! join us this Tuesday, October 29th at 9AM in the BME Library (MR5 2019). Alex…
Research Club Alert and FREE BREAKFAST!! join us this Tuesday, October 29th at 9AM in the BME Library (MR5 2019). Alex Mathew will be presenting "Dermatologist-level classification of skin cancer with deep neural networks." #MachineLearning #UVA #DataScience
RT @Age_Matters: Dermatologist-level classification of skin cancer with deep neural networks - game-changing paper quoted at #RCPIAC19 htt…
RT @Age_Matters: Dermatologist-level classification of skin cancer with deep neural networks - game-changing paper quoted at #RCPIAC19 htt…
Dermatologist-level classification of skin cancer with deep neural networks - game-changing paper quoted at #RCPIAC19 https://t.co/29y80jFJJD
RT @bedch: @cepcam 2017: https://t.co/tgLx6RglKF Les médecins ajoutent une règle dans l'image pour mesurer le cancer s'il y a un doute et/o…
@cepcam 2017: https://t.co/tgLx6RglKF Les médecins ajoutent une règle dans l'image pour mesurer le cancer s'il y a un doute et/ou quand c'est évident. Le DNN l'a appris. C'est subtil car il a aussi appris sur d'autres features. ~1000 citations avant que la communauté s'en rende compte
ADR 환자 데이터를 시각화하기 위해 t-SNE 처리를 해봤는데 역시나 명확한 grouping이 없다.[사진1] 유명한 피부과 병변 사진으로 classification하는 네이처 논문은 이런식으로 나옴.[사진2] 출처: Dermatologist-level classification of skin cancer with deep neural networks., Nature 2017 https://t.co/RT0HRjZCV8
Just read the paper by @SebastianThrun and team on Dermatologist-level classification of skin cancer with deep neural networks, the fact that the neural net outperforms some of the best doctors in the world just blows my mind. #DeepLearning
Hvordan kan Kunstig Intelligens hjælpe os. Dermatologist-level classification of skin cancer with deep neural networks. https://t.co/Zb16uIBYHv
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