# Probing and Inducing Clinical Modality Grounding in Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/2603.19844 · HTML: https://mlsanity.com/paper/2603.19844/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 15 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [ReZero is All You Need: Fast Convergence at Large Depth](https://mlsanity.com/paper/2003.04887/) (2020)
- [Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities](https://mlsanity.com/paper/2104.06231/) (2021)
- [A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation](https://mlsanity.com/paper/2111.13300/) (2021)
- [ResiDual: Transformer with Dual Residual Connections](https://mlsanity.com/paper/2304.14802/) (2023)

## Abstract (arXiv)

Multimodal MRI provides complementary information for brain tumor segmentation, but high accuracy alone does not reveal whether a model relies on clinically appropriate imaging evidence. Radiologists use different sequences for different findings, such as T1ce for enhancing tumor and FLAIR for peritumoral edema. Yet most 3D segmentation networks fuse MRI sequences through fixed connections, regardless of patient, region, or depth. We frame multimodal segmentation as grounded multimodal reasoning and introduce (i) a model agnostic probing framework combining per region modality ablation, Dominant Sensitivity Enhancement (DSE), and gate trajectory analysis to reveal sequence reliance, and (ii) evidence that adaptive Hyper-Connections (HC) induce clinically aligned modality reliance without modality supervision. Across nnU Net, SwinUNETR, and VT-UNet on BraTS 2021, most HC configurations increase reliance on clinically appropriate sequences. This interpretable pattern recurs across all three backbones, suggesting it arises from adaptive connections rather than a single architecture. HC also improves segmentation accuracy at negligible parameter cost. On validation, the best HC configuration increases mean Dice by 0.43 to 0.81 percentage points across the three backbones. On the held-out test set, it reduces mean HD95 by 1.17 to 1.78 mm (approximately 20 to 27 percent) for nnU-Net and VT UNet, adding at most 0.53 percent parameters. Our framework offers an inspectable measure of clinical grounding and extends naturally to multimodal medical foundation models.