# Transfer Learning with Edge Attention for Prostate MRI Segmentation

arXiv: https://arxiv.org/abs/1912.09847 · HTML: https://mlsanity.com/paper/1912.09847/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation](https://mlsanity.com/paper/2104.03768/) (2021)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning](https://mlsanity.com/paper/2403.02566/) (2024)
- [Comprehensive study of good model training for prostate segmentation in volumetric MRI](https://mlsanity.com/paper/2208.13671/) (2022)

## Abstract (arXiv)

Prostate cancer is one of the common diseases in men, and it is the most common malignant tumor in developed countries. Studies have shown that the male prostate incidence rate is as high as 2.5% to 16%, Currently, the inci-dence of prostate cancer in Asia is lower than that in the West, but it is increas-ing rapidly. If prostate cancer can be found as early as possible and treated in time, it will have a high survival rate. Therefore, it is of great significance for the diagnosis and treatment of prostate cancer. In this paper, we propose a trans-fer learning method based on deep neural network for prostate MRI segmenta-tion. In addition, we design a multi-level edge attention module using wavelet decomposition to overcome the difficulty of ambiguous boundary in prostate MRI segmentation tasks. The prostate images were provided by MICCAI Grand Challenge-Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset.