Last Posted: Jul 20, 2021
- Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy.
Campanella Gabriele et al. The Journal of investigative dermatology 2021
- The landscape of driver mutations in cutaneous squamous cell carcinoma
D Chang et al, NPJ Genomic Medicine, July 16, 2021
- Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.
Höhn Julia et al. Journal of medical Internet research 2021 23(7) e20708
- Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.
Alsaade Fawaz Waselallah et al. Computational and mathematical methods in medicine 2021 20219998379
- A machine-learning modified CART algorithm informs Merkel cell carcinoma prognosis.
Cheraghlou Shayan et al. The Australasian journal of dermatology 2021
- AI-based smartphone apps for risk assessment of skin cancer need more evaluation and better regulation.
Matin Rubeta N et al. British journal of cancer 2021
- Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model.
Gentile Francesco et al. Diagnostics (Basel, Switzerland) 2021 11(2)
- Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.
Goyal Manu et al. Computers in biology and medicine 2020 Oct 127104065
- Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study.
Pérez Eduardo et al. Medical image analysis 2020 Oct 67101858
- Cancer Risk Perceptions Among People Who Check Their Skin for Skin Cancer: Results from the 2017 U.S. Health Information National Trends Survey (HINTS).
Merten Julie Williams et al. Journal of cancer education : the official journal of the American Association for Cancer Education 2020 Sep
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Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.