  Binary breast cancer tumor segmentation with Magnetic Resonance Imaging (MRI)
data is typically trained and evaluated on private medical data, which makes
comparing deep learning approaches difficult. We propose a benchmark
(BC-MRI-SEG) for binary breast cancer tumor segmentation based on publicly
available MRI datasets. The benchmark consists of four datasets in total, where
two datasets are used for supervised training and evaluation, and two are used
for zero-shot evaluation. Additionally we compare state-of-the-art (SOTA)
approaches on our benchmark and provide an exhaustive list of available public
breast cancer MRI datasets. The source code has been made available at
https://irulenot.github.io/BC_MRI_SEG_Benchmark.
