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Genomics and Precision Health Database


Genomics & Health Impact Scan Database|Search|PHGKB
Last data update: Dec 28, 2020. (Total: 37862 Documents since 2012)
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Records 1-30 (of 42 Record(s))
Query Trace: Melanoma[original query]>>Non-Genomics Precision Health [Category]
Publication
Melanoma and Nevus Skin Lesion Classification Using Handcraft and Deep Learning Feature Fusion via Mutual Information Measures.
Almaraz-Damian Jose-Agustin et al. Entropy (Basel, Switzerland) 2020 Apr 22(4)
Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.
Johannet Paul et al. Clinical cancer research : an official journal of the American Association for Cancer Research 2020 Nov
Artificial intelligence for melanoma diagnosis: a review of published studies until 2020.
Tschandl Philipp et al. Giornale italiano di dermatologia e venereologia : organo ufficiale, Societa italiana di dermatologia e sifilografia 2020 Nov
Using deep learning to predict anti-PD-1 response in melanoma and lung cancer patients from histopathology images.
Hu Jing et al. Translational oncology 2020 Oct 14(1) 100921
Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study.
Pérez Eduardo et al. Medical image analysis 2020 Oct 67101858
Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm-Evaluation of Diagnostic Performance.
Wuestemann Jan et al. Cancers 2020 Sep 12(9)
Review of medical image recognition technologies to detect melanomas using neural networks.
Efimenko Mila et al. BMC bioinformatics 2020 Sep 21(Suppl 11) 270
Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials.
Schperberg Alexander V et al. International journal of cancer 2020 Aug
User satisfaction with a smartphone-compatible, artificial intelligence-based cutaneous pigmented lesion evaluator.
Po Harvey Chin Yen et al. Computer methods and programs in biomedicine 2020 Jul 195105649
Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective.
Jutzi Tanja B et al. Frontiers in medicine 2020 7233
Deep Learning Approaches Towards Skin Lesion Segmentation and Classification from Dermoscopic Images - A Review.
Baig Ramsha et al. Current medical imaging 2020 16(5) 513-533
Machine Learning in Melanoma Diagnosis. Limitations About to be Overcome.
González-Cruz C et al. Actas dermo-sifiliograficas 2020 Apr
Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening: A Qualitative Study.
Nelson Caroline A et al. JAMA dermatology 2020 Mar
DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images.
Nasiri Sara et al. BMC bioinformatics 2020 Mar 21(Suppl 2) 84
Toward a precision behavioral medicine approach to addressing high-risk sun exposure: a qualitative analysis.
Stump Tammy K et al. JAMIA open 2019 Dec 2(4) 547-553
Multiphoton microscopy of the dermoepidermal junction and automated identification of dysplastic tissues with deep learning.
Huttunen Mikko J et al. Biomedical optics express 2020 Jan 11(1) 186-199
Artificial intelligence for melanoma diagnosis: how can we deliver on the promise?
Mar V J et al. Annals of oncology : official journal of the European Society for Medical Oncology 2019 Dec 30(12) e1-e3
Skin cancer diagnosis based on optimized convolutional neural network.
Zhang Ni et al. Artificial intelligence in medicine 2020 Jan 102101756
Characterizing the Role of Dermatologists in Developing AI for Assessment of Skin Cancer: A Systematic Review.
Zakhem George A et al. Journal of the American Academy of Dermatology 2020 Jan
What is AI? Applications of artificial intelligence to dermatology.
Du-Harpur X et al. The British journal of dermatology 2020 Jan
Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy.
Phillips Michael et al. Dermatology practical & conceptual 2020 10(1) e2020011
Augmented Decision Making for Acral Lentiginous Melanoma Detection Using Deep Convolutional Neural Networks.
Lee S et al. Journal of the European Academy of Dermatology and Venereology : JEADV 2020 Jan
Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.
Haenssle H A et al. Annals of oncology : official journal of the European Society for Medical Oncology 2018 29(8) 1836-1842
Superpixel-Based Conditional Random Fields (SuperCRF): Incorporating Global and Local Context for Enhanced Deep Learning in Melanoma Histopathology.
Zormpas-Petridis Konstantinos et al. Frontiers in oncology 2019 91045
Deep learning based on standard H&E images of primary melanoma tumors identifies patients at risk for visceral recurrence and death.
Kulkarni Prathamesh M et al. Clinical cancer research : an official journal of the American Association for Cancer Research 2019 Oct
Superior skin cancer classification by the combination of human and artificial intelligence.
Hekler Achim et al. European journal of cancer (Oxford, England : 1990) 2019 Sep 120114-121
Artificial intelligence for melanoma diagnosis: how can we deliver on the promise?
Mar V J et al. Annals of oncology : official journal of the European Society for Medical Oncology 2018 29(8) 1625-1628
Whole-body magnetic resonance imaging (WB-MRI) in oncology: recommendations and key uses.
Petralia Giuseppe et al. La Radiologia medica 2019 Mar 124(3) 218-233
Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition.
Winkler Julia K et al. JAMA dermatology 2019 Aug
Deep neural networks are superior to dermatologists in melanoma image classification.
Brinker Titus J et al. European journal of cancer (Oxford, England : 1990) 2019 Aug 11911-17
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