Published March 8, 2023
| Version v1
Dataset
Open
Basal cell carcinoma diagnosis with fusion of deep learning and telangiectasia features
Authors/Creators
- 1. Missouri University of Science & Technology
- 2. Ford Motor Company
- 3. University of Missouri - Columbia
- 4. University of Missouri - Columbia
- 5. A.T. Still University of Health Sciences
- 6. University of Missouri - Kansas City
- 7. S&A Technologies
Description
Telangiectasia masks dataset created on a subset of the ISIC18, ISIC19 training datasets and the NIH study dataset R43 CA153927-01 and CA101639-02A2. All annotations are for Basal Cell Carcinoma lesions. This dataset was used in the publication: " Basal cell carcinoma diagnosis with fusion of deep learning and telangiectasia features " (to be submitted). This is an expanded dataset that was initially used in “A Deep Learning Approach to Detect Blood Vessels in Basal Cell Carcinoma”.
Files
images.zip
Files
(171.3 MB)
| Name | Size | Download all |
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md5:6b1948c518bcd926ea37b7831ba14fb4
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119.4 MB | Preview Download |
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md5:dc452fe73dcf8d0d698178012e0d2076
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51.9 MB | Preview Download |
Additional details
References
- BCN_20000 Dataset: (c) Department of Dermatology, Hospital Clínic de Barcelona
- HAM10000 Dataset: (c) by ViDIR Group, Department of Dermatology, Medical University of Vienna; https://doi.org/10.1038/sdata.2018.161
- MSK Dataset: (c) Anonymous; https://arxiv.org/abs/1710.05006; https://arxiv.org/abs/1902.03368
- NIH study dataset R43 CA153927-01 and CA101639-02A2