Discontinued support and maintenance for this repository. This online API does not work anymore. Thanks for all the contributions and engagement!

This is the online RESTful API of the region-based CNN model (https://rcnn1.modelderm.com) used for articles published in JAMA Dermatology (http://doi.org/10.1001/jamadermatol.2019.3807) and (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003381). The model API can be used only for the research purposes. The submitted images will be transferred for the cloud analysis, but the images are not stored. Please register at http://bbs.modelderm.com to use the API. The API is open to the computer logged in.

Requirement

0. Please register (https://bbs.modelderm.com). Only the researcher with email on pubmed.gov will be allowed to use the API.

1. Download and install python 3 (ex. anaconda version 3.8 64 bit) at https://www.anaconda.com/products/individual#Downloads

   

   

   Please be sure to add the system PATH.

   

2. Install opencv

   
    (windows) pip install opencv-python
    
    (linux) sudo pip install opencv-python
    

   

3. Download the API file from github and extract the zip.

   

How to Use

1. There are 10 example images in the folder “/examples”. (images under the CC-BY-NC license)

   

2. When performing the test for a single image, run the following command:

   
    python test.py [test_jpg file] [save_folder; default="RESULT"]
    

   

   Or you can also simply run “test1.bat”

   

3. When performing the test for all images contained in a folder, run the following command:

   
    python test.py [test_folder] [save_folder; default="RESULT"]
    

   https://github.com/whria78/modelderm_rcnn_api/blob/master/test.log

   Or you can also simply run “test2.bat”

   

4. The results can be found in the folder “/RESULT”.

   

5. The results are also stored in the form of “.csv” as below. They are listed in the order of “x0, y0, x1, y1, malignancy output, prediction”. The upper-left corner is 'x0, y0' and the lower-right corner is 'x1, y1'. In prediction, a hyphen (-) means that the lesion is nonspecific.

   https://github.com/whria78/modelderm_rcnn_api/blob/master/RESULT/result.csv

Waiting Policy

The current test server (GPU = 1060x1, 1050tix1) requires 30 ~ 60 seconds to analyze one image and is capable of analyzing 20,000~30,000 images weekly, When there are more than three users online, heavier users will have to wait until active analyses are completed. The test server uses the IP address to identify each user.

Contact Information

If you have any problem in using the algorithm, please contact Han Seung Seog (whria78@gmail.com).

Citation

1. JAMA Dermatology 2019 - a model development and validation study

@article{10.1001/jamadermatol.2019.3807,
    author = {Han, Seung Seog and Moon, Ik Jun and Lim, Woohyung and Suh, In Suck and Lee, Sam Yong and Na, Jung-Im and Kim, Seong Hwan and Chang, Sung Eun},
    title = "{Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network}",
    journal = {JAMA Dermatology},
    year = {2020},
}

2. PLOS Medicine 2020 - a retrospective cohort study

@article{10.1371/journal.pmed.1003381,
  author={Han, Seung Seog and Moon, Ik Jun and Kim, Seong Hwan and Na, Jung-Im and Kim, Myoung Shin and Park, Gyeong Hun and Park, Ilwoo and Kim, Keewon and Lim, Woohyung and Lee, Ju Hee and others},
  title={Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study},
  journal={PLoS medicine},
  year={2020},
}