Code to reproduce the results for the paper "(De)Constructing Bias on Skin Lesions Datasets" in ISIC Skin Image Analysis Workshop @ CVPR 2019. Link to the paper.
We used nvidia-docker for all experiments. Run the following command to configure a container:
nvidia-docker run -ti --userns=host --shm-size 8G -v /home/deconstructing-bias-skin-lesion/:/deconstructing-bias-skin-lesion/ --name deconstructingbias nvidia/cuda:9.1-devel-ubuntu16.04 /bin/bash
apt-get install imagemagick git python3 python3-pip
pip3 install -r requirements.txt
We used the Interactive Atlas of Dermoscopy and data from the ISIC Archive for our experiments. You need to download the 2018 ISIC Challenge training set. Download images and ground truth for tasks 1 and 2.
To create the disturbed, and the attribute sets used in our experiments, please check the scripts folder.
All the exact splits used are available in folders atlas-csv and isic-csv.
To train and evaluate the network, refer to the scripts run_isic_7030_all.sh and run_isic_rgbm_7030.sh.
@inproceedings{bissoto19deconstructing,
author = {Alceu Bissoto and Michel Fornaciali and Eduardo Valle and Sandra Avila},
title = {({D}e){C}onstructing Bias on Skin Lesion Datasets},
booktitle = {ISIC Skin Image Anaylsis Workshop, 2019 {IEEE} Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
year = {2019},
}
A. Bissoto and S. Avila are partially funded Google LARA 2018. A. Bissoto is also partially funded by CNPq. E. Valle is partially funded by a CNPq PQ-2 grant (311905/2017-0). This work was funded by grants from CNPq (424958/2016-3), FAPESP (2017/16246-0) and FAEPEX (3125/17). The RECOD Lab receives addition funds from FAPESP, CNPq, and CAPES. We gratefully acknowledge NVIDIA for the donation of GPU hardware.