Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)
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- Type
- article
- Published
- 2016-05-04
- Cited by
- 2,793
- References
- 46
- Access
- Open access
- OpenAlex
- https://openalex.org/W2346705140
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10768153
Keywords
Artificial intelligence, Skin cancer, Skin lesion, Computer science, Segmentation
References
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- A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data
- Dermoscopy: the pattern analysis
- Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features.
- Computer-Aided Diagnosis of Melanoma Using Border- and Wavelet-Based Texture Analysis
- The US dermatology workforce: a specialty remains in shortage.
- Incidence Estimate of Nonmelanoma Skin Cancer (Keratinocyte Carcinomas) in the U.S. Population, 2012.
- Dermoscopy of pigmented skin lesions.
- The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy.
- Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests.
- PH2 - A dermoscopic image database for research and benchmarking
- Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features
- Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology
- Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis.
Cited by
- Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network
- Segmentation of Both Diseased and Healthy Skin From Clinical Photographs in a Primary Care Setting
- An intelligent decision support system for skin cancer detection from dermoscopic images
- Deep learning ensembles for melanoma recognition in dermoscopy images
- Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
- Combining deep learning and hand-crafted features for skin lesion classification
- Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification
- Skin Lesion Classification Using Hybrid Deep Neural Networks
- Automated melanoma recognition in dermoscopic images based on extreme learning machine (ELM)
- A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
- Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholding
- Deep Bayesian Active Learning with Image Data
- Skin lesion segmentation based on preprocessing, thresholding and neural networks
- Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features
- Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble
- A general algorithm for automatic lesion segmentation in dermoscopy images
- Global and Local Information Based Deep Network for Skin Lesion Segmentation
- Skin lesion detection based on an ensemble of deep convolutional neural network
- Patchnet: Interpretable Neural Networks for Image Classification
- Embeddable Real Time Tool for Automatic Skin Lesions Characterization
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