This repo contains the code accompanying our paper A novel focal Tversky loss function and improved Attention U-Net for lesion segmentation accepted at ISBI 2019.
TL;DR We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation. Additionally, we incorporate architectural changes that benefit small lesion segmentation.
Figure 1 in the paper is parametrized by the function which is incorrectly depicted in Equation 4.
The code in this repository follows the parametrization: which is in line with Equation 4. I apologize for the confusion! Both parametrizations have the same effect on the gradients however I found the latter one to be more stable and so that is the loss function presented in this repo.
We utilize attention gating in this repo which follows from Ozan Oktan and his collaborators. The workflow is depicted below:
Training files for the ISIC2018 and BUS2017 Dataset B have been added.
If training with ISIC2018, create 4 folders: orig_raw (not used in this code), orig_gt, resized-train, resized-gt, for full
resolution input images, ground truth and resized images at 192x256 resolution, respectively.
If training with BUS2017, create 2 folders: original and gt for input data and ground truth data. In the bus_train.py script, images
will be resampled to 128x128 resolution.
If you find this code useful, please consider citing our work:
@article{focal-unet,
title={A novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation},
author={Abraham, Nabila and Khan, Naimul Mefraz},
journal={arXiv preprint arXiv:1810.07842},
year={2018}
}