  We propose a generalized focal loss function based on the Tversky index to
address the issue of data imbalance in medical image segmentation. Compared to
the commonly used Dice loss, our loss function achieves a better trade off
between precision and recall when training on small structures such as lesions.
To evaluate our loss function, we improve the attention U-Net model by
incorporating an image pyramid to preserve contextual features. We experiment
on the BUS 2017 dataset and ISIC 2018 dataset where lesions occupy 4.84% and
21.4% of the images area and improve segmentation accuracy when compared to the
standard U-Net by 25.7% and 3.6%, respectively.
