BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark

Anthony Bilic, Chen Chen
4/21/2024
eess.IVcs.CV

No official code found. Best implementation: irulenot/BC_MRI_SEG (28 stars, last commit 2024-04-21).

Abstract

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.

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irulenot/BC_MRI_SEGCommunity75%

BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark

283PythonApr 16, 20242 years ago

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Cite this paper

@article{bilic2024bcmriseg,
  title  = {BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark},
  author = {Anthony Bilic and Chen Chen},
  year   = {2024},
  eprint = {2404.13756},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.13756v2}
}

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