Cargando…
Lesion identification and malignancy prediction from clinical dermatological images
We consider machine-learning-based lesion identification and malignancy prediction from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal o...
Autores principales: | Xia, Meng, Kheterpal, Meenal K., Wong, Samantha C., Park, Christine, Ratliff, William, Carin, Lawrence, Henao, Ricardo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508136/ https://www.ncbi.nlm.nih.gov/pubmed/36151257 http://dx.doi.org/10.1038/s41598-022-20168-w |
Ejemplares similares
-
Use of convolutional neural networks in skin lesion analysis using real world image and non-image data
por: Wong, Samantha C., et al.
Publicado: (2022) -
Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations
por: Jeong, Hyeon Ki, et al.
Publicado: (2022) -
Current Landscape of Generative Adversarial Networks for Facial Deidentification in Dermatology: Systematic Review and Evaluation
por: Park, Christine, et al.
Publicado: (2022) -
Annular Lesions in Dermatology
por: Narayanasetty, Naveen Kikkeri, et al.
Publicado: (2013) -
Monkeypox and dermatological lesions()
por: Kleebayoon, Amnuay, et al.
Publicado: (2023)