# Automatic Brain Tumor Segmentation with Scale Attention Network

arXiv: https://arxiv.org/abs/2011.03188 · HTML: https://mlsanity.com/paper/2011.03188/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps](https://mlsanity.com/paper/2312.02608/) (2023)
- [Cross-dimensional transfer learning in medical image segmentation with deep learning](https://mlsanity.com/paper/2307.15872/) (2023)
- [Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images](https://mlsanity.com/paper/2209.09930/) (2022)
- [Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy](https://mlsanity.com/paper/2505.14507/) (2025)
- [Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation](https://mlsanity.com/paper/2401.06180/) (2024)
- [SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation](https://mlsanity.com/paper/2401.13403/) (2024)
- [Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI](https://mlsanity.com/paper/2401.15434/) (2024)
- [Fairboard: a quantitative framework for equity assessment of healthcare models](https://mlsanity.com/paper/2604.09656/) (2026)

## Abstract (arXiv)

Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. Multimodal Brain Tumor Segmentation Challenge 2020 (BraTS 2020) provides a common platform for comparing different automatic algorithms on multi-parametric Magnetic Resonance Imaging (mpMRI) in tasks of 1) Brain tumor segmentation MRI scans; 2) Prediction of patient overall survival (OS) from pre-operative MRI scans; 3) Distinction of true tumor recurrence from treatment related effects and 4) Evaluation of uncertainty measures in segmentation. We participate the image segmentation challenge by developing a fully automatic segmentation network based on encoder-decoder architecture. In order to better integrate information across different scales, we propose a dynamic scale attention mechanism that incorporates low-level details with high-level semantics from feature maps at different scales. Our framework was trained using the 369 challenge training cases provided by BraTS 2020, and achieved an average Dice Similarity Coefficient (DSC) of 0.8828, 0.8433 and 0.8177, as well as 95% Hausdorff distance (in millimeter) of 5.2176, 17.9697 and 13.4298 on 166 testing cases for whole tumor, tumor core and enhanced tumor, respectively, which ranked itself as the 3rd place among 693 registrations in the BraTS 2020 challenge.