# SYNAPSE-Net: A Unified Framework with Lesion-Aware Hierarchical Gating for Robust Segmentation of Heterogeneous Brain Lesions

arXiv: https://arxiv.org/abs/2510.26961 · HTML: https://mlsanity.com/paper/2510.26961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 65 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 23 of 23)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset](https://mlsanity.com/paper/2206.06694/) (2022)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [Vox2Vox: 3D-GAN for Brain Tumour Segmentation](https://mlsanity.com/paper/2003.13653/) (2020)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Modality-Pairing Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2010.09277/) (2020)
- [Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images](https://mlsanity.com/paper/1802.05203/) (2018)
- [Dual Cross-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2303.17696/) (2023)
- [Automatic Post-Stroke Lesion Segmentation on MR Images using 3D Residual Convolutional Neural Network](https://mlsanity.com/paper/1911.11209/) (2019)
- [Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images](https://mlsanity.com/paper/2211.01885/) (2022)
- [DR-Unet104 for Multimodal MRI brain tumor segmentation](https://mlsanity.com/paper/2011.02840/) (2020)
- [Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation](https://mlsanity.com/paper/2303.01332/) (2023)
- [White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections](https://mlsanity.com/paper/1803.06782/) (2018)
- [Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation](https://mlsanity.com/paper/2101.00490/) (2021)

## Abstract (arXiv)

Automatic segmentation of diverse heterogeneous brain lesions using multi-modal MRI is a challenging problem in clinical neuroimaging, mainly because of the lack of generalizability and high prediction variance of pathology-specific deep learning models. In this work, we propose a unified and adaptive multi-stream framework called SYNAPSE-Net to perform robust multi-pathology segmentation with reduced performance variance. The framework is based on multi-stream convolutional encoders with global context modeling and a cross-modal attention fusion strategy to ensure stable and effective multi-modal feature integration. It also employs a variance-aware training strategy to enhance the robustness of the network across diverse tasks. The framework is extensively validated using three public challenge datasets: WMH MICCAI 2017, ISLES 2022, and BraTS 2020. The results show consistent improvements in boundary accuracy, delineation quality, and stability across diverse pathologies. This proposed framework achieved a high Dice similarity coefficient (DSC) of 0.831 and a low Hausdorff distance at the 95th percentile (HD95) of 3.03 on the WMH MICCAI 2017 dataset. It also achieved the lowest HD95 of 9.69 on the ISLES 2022 dataset and the highest tumor core DSC of 0.8651 on the BraTS 2020 dataset. These results validate the robustness of the proposed framework in providing a clinically relevant computer-aided solution for automated brain lesion segmentation. Source code and pretrained models are publicly available at https://github.com/mubid-01/SYNAPSE-Net-pre.