Computerized analysis of pigmented skin lesions: A review
By K. Korotkov, Rafael García. Artif. Intell. Medicine, 2012
385 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oncology
View Paper: https://www.semanticscholar.org/paper/bb3a2c26f412b502a728a555309717753bbb777d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jorge S. Marques
- Michael Biehl
- N. Petkov
- André R. S. Marçal
- Jeremy Kawahara
- Ghassan Hamarneh
- Aïcha BenTaieb
- Yading Yuan
In Computer Vision and Pattern Recognition
- Jorge S. Marques
- Teresa Mendonça
- Michael Biehl
- Ioannis Giotis
- N. Petkov
- André R. S. Marçal
- M. Jonkman
- Jeremy Kawahara
In Oncology
Citations in the corpus
- PH2 - A dermoscopic image database for research and benchmarking (2013, methodology): “They can also be used as an additional tool by non-experienced clinicians to perform a preliminary evaluation and to improve the patient follow-up procedure [6].”
- Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance (2017, methodology): “A review of the state-of-the-art methods up to 2012 is presented in [5].”
- Deep features to classify skin lesions (2016, background): “distinguishing between cancer and noncancerous melanoma skin images has attracted considerable research [2].”
- MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images (2015, background): “Korotkov and Garcia (2012) recently presented an extensive and comprehensive review regarding the computerized analysis of pigmented skin lesions.”