# Scalable multimodal convolutional networks for brain tumour segmentation

arXiv: https://arxiv.org/abs/1706.08124 · HTML: https://mlsanity.com/paper/1706.08124/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2017.

## Built on by (most cited first, 3 of 3)

- [Cross-Modality Deep Feature Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2201.02356/) (2022)
- [DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2505.11921/) (2025)
- [A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers](https://mlsanity.com/paper/2310.05572/) (2023)

## Abstract (arXiv)

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.