The data of ISIC2019 is a dermoscopy skin lesion image.
-
Download and unzip the dataset from Skin Lesion Images for Melanoma Classification.
-
Rearrange the images with the same class (each class uses a folder with the class name).
-
Random oversample using
lib/tools/oversampling.py. -
IH undersample using
lib/tools/undersampling.py. -
Create the 10 split subsets for cross-validation (the augmented data and its original image should be on the same subset).
-
The output data structure should be:
${HierAttn_ROOT} |-- dataset `-- |-- IHISIC20000 `-- |--- split1 | |--- ack_1.jpg | |--- ack_2.jpg | |--- ... | |--- bcc_1.jpg | |--- ... |--- split2 | |--- ack_1.jpg | |--- ack_2.jpg | |--- ... | |--- bcc_1.jpg | |--- ... |--- ...
The data of PAD20 is a smartphone skin lesion image.
-
Download and unzip the dataset from PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones.
-
Rearrange the images with the same class (each class uses a folder with the class name).
-
Random oversample using
lib/tools/oversampling.py. -
IH undersample using
lib/tools/undersampling.py. -
Create the 10 split subsets for cross-validation (the augmented data and its original image should be on the same subset).
-
The output data structure should be:
${HierAttn_ROOT} |-- dataset `-- |-- IHPAD3000 `-- |--- split1 | |--- ack_1.jpg | |--- ack_2.jpg | |--- ... | |--- bcc_1.jpg | |--- ... |--- split2 | |--- ack_1.jpg | |--- ack_2.jpg | |--- ... | |--- bcc_1.jpg | |--- ... |--- ...
Please cite the corresponding references if you use the datasets and our data pre-processing codes.
@article{tschandl2018ham10000,
title={The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions},
author={Tschandl, Philipp and Rosendahl, Cliff and Kittler, Harald},
journal={Scientific data},
volume={5},
number={1},
pages={1--9},
year={2018},
publisher={Nature Publishing Group}
}
@inproceedings{codella2018skin,
title={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)},
author={Codella, Noel CF and Gutman, David and Celebi, M Emre and Helba, Brian and Marchetti, Michael A and Dusza, Stephen W and Kalloo, Aadi and Liopyris, Konstantinos and Mishra, Nabin and Kittler, Harald and others},
booktitle={2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018)},
pages={168--172},
year={2018},
organization={IEEE}
}
@article{combalia2019bcn20000,
title={Bcn20000: Dermoscopic lesions in the wild},
author={Combalia, Marc and Codella, Noel CF and Rotemberg, Veronica and Helba, Brian and Vilaplana, Veronica and Reiter, Ofer and Carrera, Cristina and Barreiro, Alicia and Halpern, Allan C and Puig, Susana and others},
journal={arXiv preprint arXiv:1908.02288},
year={2019}
}
@article{pacheco2020pad,
title={PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones},
author={Pacheco, Andre GC and Lima, Gustavo R and Salom{\~a}o, Amanda S and Krohling, Breno and Biral, Igor P and de Angelo, Gabriel G and Alves Jr, F{\'a}bio CR and Esgario, Jos{\'e} GM and Simora, Alana C and Castro, Pedro BC and others},
journal={Data in brief},
volume={32},
pages={106221},
year={2020},
publisher={Elsevier}
}
@ARTICLE{10230242,
author={Dai, Wei and Liu, Rui and Wu, Tianyi and Wang, Min and Yin, Jianqin and Liu, Jun},
journal={IEEE Journal of Biomedical and Health Informatics},
title={Deeply Supervised Skin Lesions Diagnosis With Stage and Branch Attention},
year={2024},
volume={28},
number={2},
pages={719-729},
keywords={Skin;Lesions;Feature extraction;Convolution;Transformers;Training;Computational modeling;Attention;deep supervision;disease classification;skin lesion;vision transformer},
doi={10.1109/JBHI.2023.3308697}}