This paper is in the following e-collection/theme issue:

Machine Learning from Digital Images in Dermatology (14) Clinical Information and Decision Making (1694) Digital Photography and Imaging in Dermatology (40) Machine Learning (1780) Cancer Prognosis Models and Machine Learning (30)

Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42129, first published .
Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study

Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study

Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study

Authors of this article:

Mathias Gassner1, 2 Author Orcid Image ;   Javier Barranco Garcia3 Author Orcid Image ;   Stephanie Tanadini-Lang3 Author Orcid Image ;   Fabio Bertoldo4 Author Orcid Image ;   Fabienne Fröhlich4 Author Orcid Image ;   Matthias Guckenberger3 Author Orcid Image ;   Silvia Haueis4 Author Orcid Image ;   Christin Pelzer4 Author Orcid Image ;   Mauricio Reyes5, 6 Author Orcid Image ;   Patrick Schmithausen4 Author Orcid Image ;   Dario Simic4 Author Orcid Image ;   Ramon Staeger4 Author Orcid Image ;   Fabio Verardi4 Author Orcid Image ;   Nicolaus Andratschke3 Author Orcid Image ;   Andreas Adelmann7 Author Orcid Image ;   Ralph P Braun8 Author Orcid Image
Article Authors Cited by (7) Tweetations (3) Metrics

Journals

  1. Hire D, Patil A, Charles P. Efficient rotated and scaled digital image retrieval model using deep learning-based hybrid features extraction. Multimedia Tools and Applications 2023;83(12):34733 View
  2. Zhang J, Cosma G, Bugby S, Watkins J. Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets. Journal of Imaging 2023;9(12):277 View
  3. de Luzuriaga A. Improving data participation for the development of artificial intelligence in dermatology. Clinics in Dermatology 2024;42(5):447 View
  4. Allen K, Yawson A, Haggenmüller S, Kather J, Brinker T. Human-centered AI as a framework guiding the development of image-based diagnostic tools in oncology: a systematic review. ESMO Real World Data and Digital Oncology 2024;6:100077 View
  5. Akçiçek M, Karaduman M, Petik B, Ünlü S, Mutlu H, Yildirim M. Detection of Acromion Types in Shoulder Magnetic Resonance Image Examination with Developed Convolutional Neural Network and Textural-Based Content-Based Image Retrieval System. Journal of Clinical Medicine 2025;14(2):505 View
  6. Cece A, Agresti M, De Falco N, Sperlongano P, Moccia G, Luongo P, Miele F, Allaria A, Torelli F, Bassi P, Sciarra A, Avenia S, Della Monica P, Colapietra F, Di Domenico M, Docimo L, Parmeggiani D. Role of Artificial Intelligence in Thyroid Cancer Diagnosis. Journal of Clinical Medicine 2025;14(7):2422 View

Conference Proceedings

  1. Boukerma R, Boucheham B, Bougueroua S. 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS). Optimized Deep Features for Colon Histology Image Retrieval View