Fed-ISIC 2019

The dataset used in this repo comes from the ISIC2019 challenge and the HAM1000 database. We do not own the copyright of the data, everyone using those datasets should abide by their licences (see below) and give proper attribution to the original authors.

Dataset description

The following table provides a data sheet:

	Dataset description
Description	Dataset from the ISIC 2019 challenge, we keep images for which the datacenter can be extracted.
Dataset	23,247 images of skin lesions ((9930/2483), (3163/791), (2691/672), (1807/452), (655/164), (351/88))
Centers	6 centers (BCN, HAM_vidir_molemax, HAM_vidir_modern, HAM_rosendahl, MSK, HAM_vienna_dias)
Task	Multiclass image classification

License

The full licence for ISIC2019 is CC-BY-NC 4.0.

In order to extract the origins of the images in the HAM10000 Dataset (cited above), we store in this repository a copy of the original HAM10000 metadata file. Please find attached the link to the full licence and dataset terms for the HAM10000 Dataset.

Please first accept the licences on the HAM10000 and ISIC2019 dataset pages before going through the following steps.

Ethics

As per the Terms of Use of the website hosting the dataset, one of the requirements for this datasets to have been hosted is that it is properly de-identified in accordance with the applicable requirements and legislations.

Data

To download the ISIC 2019 training data and extract the original datacenter information for each image, First cd into the dataset_creation_scripts folder:

cd flamby/datasets/fed_isic2019/dataset_creation_scripts

then run:

python download_isic.py --output-folder /path/to/user/folder

The file train_test_split contains the train/test split of the images (stratified by center).

Image preprocessing

To preprocess and resize images, run:

python resize_images.py

This script will resize all images so that the shorter edge of the resized image is 224px and the aspect ratio of the input image is maintained. Color constancy is added in the preprocessing.

Be careful: in order to allow for augmentations, images aspect ratios are conserved in the preprocessing so images are rectangular with a fixed width so they all have different heights. As a result they cannot be batched without cropping them to a square. An example of such a cropping strategy can be found in the benchmark found below.

Using the dataset

Now that the dataset is ready for use you can load it using the low or high-level API by running in a python shell:

from flamby.datasets.fed_isic2019 import FedIsic2019

# To load the first center as a pytorch dataset
center0 = FedIsic2019(center=0, train=True)
# To load the second center as a pytorch dataset
center1 = FedIsic2019(center=1, train=True)
# To load the 3rd center ...

# To sample batches from each of the local datasets use the traditional pytorch API
from torch.utils.data import DataLoader as dl

X, y = iter(dl(center0, batch_size=16, shuffle=True, num_workers=0)).next()

More informations on how to train model and handle flamby datasets in general are available in the Quickstart

Baseline training and evaluation in a pooled setting

To train and evaluate a classification model for the pooled dataset, run:

python benchmark.py --GPU 0 --workers 4

References

The “ISIC 2019: Training” is the aggregate of the following datasets:

BCN_20000 Dataset: (c) Department of Dermatology, Hospital Clínic de Barcelona

HAM10000 Dataset: (c) by ViDIR Group, Department of Dermatology, Medical University of Vienna; HAM10000 dataset

MSK Dataset: (c) Anonymous; challenge 2017; challenge 2018

See below the full citations:

[1] Tschandl P., Rosendahl C. & Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 doi.10.1038/sdata.2018.161 (2018).

[2] Noel C. F. Codella, David Gutman, M. Emre Celebi, Brian Helba, Michael A. Marchetti, Stephen W. Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, Allan Halpern: “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)”, 2017; arXiv:1710.05006.

[3] Marc Combalia, Noel C. F. Codella, Veronica Rotemberg, Brian Helba, Veronica Vilaplana, Ofer Reiter, Allan C. Halpern, Susana Puig, Josep Malvehy: “BCN20000: Dermoscopic Lesions in the Wild”, 2019; arXiv:1908.02288. ## Acknowledgement

We thank Aman Arora for his implementation and blog that we used as a base for our own code.