These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

151 related articles for article (PubMed ID: 37632881)

  • 1. Issues in Melanoma Detection: Semisupervised Deep Learning Algorithm Development via a Combination of Human and Artificial Intelligence.
    Zhang X; Xie Z; Xiang Y; Baig I; Kozman M; Stender C; Giancardo L; Tao C
    JMIR Dermatol; 2022 Dec; 5(4):e39113. PubMed ID: 37632881
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Fusing fine-tuned deep features for skin lesion classification.
    Mahbod A; Schaefer G; Ellinger I; Ecker R; Pitiot A; Wang C
    Comput Med Imaging Graph; 2019 Jan; 71():19-29. PubMed ID: 30458354
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Deep Learning Approaches Towards Skin Lesion Segmentation and Classification from Dermoscopic Images - A Review.
    Baig R; Bibi M; Hamid A; Kausar S; Khalid S
    Curr Med Imaging; 2020; 16(5):513-533. PubMed ID: 32484086
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection.
    Wang Y; Cai J; Louie DC; Wang ZJ; Lee TK
    Comput Biol Med; 2021 Oct; 137():104812. PubMed ID: 34507158
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare.
    Maqsood S; Damaševičius R
    Neural Netw; 2023 Mar; 160():238-258. PubMed ID: 36701878
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study.
    Maron RC; Hekler A; Krieghoff-Henning E; Schmitt M; Schlager JG; Utikal JS; Brinker TJ
    J Med Internet Res; 2021 Mar; 23(3):e21695. PubMed ID: 33764307
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.
    Alsaade FW; Aldhyani THH; Al-Adhaileh MH
    Comput Math Methods Med; 2021; 2021():9998379. PubMed ID: 34055044
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Identification of Novel Dermoscopic Patterns for "Featureless Melanoma": Clinical-Pathological Correlation.
    Lampitelli S; Cantisani C; Rega F; Chello C; Farnetani F; Pellacani G
    Dermatol Pract Concept; 2023 Apr; 13(2):. PubMed ID: 37196275
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents.
    Wu JT; Wong KCL; Gur Y; Ansari N; Karargyris A; Sharma A; Morris M; Saboury B; Ahmad H; Boyko O; Syed A; Jadhav A; Wang H; Pillai A; Kashyap S; Moradi M; Syeda-Mahmood T
    JAMA Netw Open; 2020 Oct; 3(10):e2022779. PubMed ID: 33034642
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Artificial Intelligence Algorithms for Benign vs. Malignant Dermoscopic Skin Lesion Image Classification.
    Brutti F; La Rosa F; Lazzeri L; Benvenuti C; Bagnoni G; Massi D; Laurino M
    Bioengineering (Basel); 2023 Nov; 10(11):. PubMed ID: 38002446
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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.
    Marchetti MA; Codella NCF; Dusza SW; Gutman DA; Helba B; Kalloo A; Mishra N; Carrera C; Celebi ME; DeFazio JL; Jaimes N; Marghoob AA; Quigley E; Scope A; Yélamos O; Halpern AC;
    J Am Acad Dermatol; 2018 Feb; 78(2):270-277.e1. PubMed ID: 28969863
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.
    Maron RC; Utikal JS; Hekler A; Hauschild A; Sattler E; Sondermann W; Haferkamp S; Schilling B; Heppt MV; Jansen P; Reinholz M; Franklin C; Schmitt L; Hartmann D; Krieghoff-Henning E; Schmitt M; Weichenthal M; von Kalle C; Fröhling S; Brinker TJ
    J Med Internet Res; 2020 Sep; 22(9):e18091. PubMed ID: 32915161
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Dermoscopy of Small Diameter Melanomas with the Diagnostic Feasibility of Selected Algorithms-A Clinical Retrospective Multicenter Study.
    Slowinska M; Kaminska-Winciorek G; Kowalska-Oledzka E; Czarnecka I; Czarnecki R; Nasierowska-Guttmejer A; Paluchowska E; Owczarek W
    Cancers (Basel); 2021 Dec; 13(23):. PubMed ID: 34885203
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions.
    Blum A; Luedtke H; Ellwanger U; Schwabe R; Rassner G; Garbe C
    Br J Dermatol; 2004 Nov; 151(5):1029-38. PubMed ID: 15541081
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI.
    Ahmad N; Shah JH; Khan MA; Baili J; Ansari GJ; Tariq U; Kim YJ; Cha JH
    Front Oncol; 2023; 13():1151257. PubMed ID: 37346069
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition.
    Winkler JK; Fink C; Toberer F; Enk A; Deinlein T; Hofmann-Wellenhof R; Thomas L; Lallas A; Blum A; Stolz W; Haenssle HA
    JAMA Dermatol; 2019 Oct; 155(10):1135-1141. PubMed ID: 31411641
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improving Artificial Intelligence-Based Diagnosis on Pediatric Skin Lesions.
    Mehta PP; Sun M; Betz-Stablein B; Halpern A; Soyer HP; Weber J; Kose K; Rotemberg V
    J Invest Dermatol; 2023 Aug; 143(8):1423-1429.e1. PubMed ID: 36804150
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review.
    Cui X; Wei R; Gong L; Qi R; Zhao Z; Chen H; Song K; Abdulrahman AAA; Wang Y; Chen JZS; Chen S; Zhao Y; Gao X
    J Am Acad Dermatol; 2019 Nov; 81(5):1176-1180. PubMed ID: 31255749
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automatic skin lesion classification based on mid-level feature learning.
    Liu L; Mou L; Zhu XX; Mandal M
    Comput Med Imaging Graph; 2020 Sep; 84():101765. PubMed ID: 32810817
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.
    Maron RC; Weichenthal M; Utikal JS; Hekler A; Berking C; Hauschild A; Enk AH; Haferkamp S; Klode J; Schadendorf D; Jansen P; Holland-Letz T; Schilling B; von Kalle C; Fröhling S; Gaiser MR; Hartmann D; Gesierich A; Kähler KC; Wehkamp U; Karoglan A; Bär C; Brinker TJ;
    Eur J Cancer; 2019 Sep; 119():57-65. PubMed ID: 31419752
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.