Variance-Penalized MC-Dropout as a Learned Smoothing Prior for Brain Tumour Segmentation

TL;DR

This paper introduces UAMSA-UNet, a Bayesian segmentation model that uses variance penalization and multi-scale attention to produce more accurate and coherent brain tumor masks while reducing computational costs.

Contribution

The paper presents a novel Bayesian U-Net with a learned smoothing prior, combining multi-scale attention and variance penalization for improved brain tumor segmentation.

Findings

Up to 3.3% Dice improvement on BraTS2023

Up to 4.5% Dice improvement on BraTS2024

42.5% reduction in FLOPs compared to U-Net++

Abstract

Brain tumor segmentation is essential for diagnosis and treatment planning, yet many CNN and U-Net based approaches produce noisy boundaries in regions of tumor infiltration. We introduce UAMSA-UNet, an Uncertainty-Aware Multi-Scale Attention-based Bayesian U-Net that in- stead leverages Monte Carlo Dropout to learn a data-driven smoothing prior over its predictions, while fusing multi-scale features and attention maps to capture both fine details and global context. Our smoothing-regularized loss augments binary cross-entropy with a variance penalty across stochas- tic forward passes, discouraging spurious fluctuations and yielding spatially coherent masks. On BraTS2023, UAMSA- UNet improves Dice Similarity Coefficient by up to 3.3% and mean IoU by up to 2.7% over U-Net; on BraTS2024, it delivers up to 4.5% Dice and 4.0% IoU gains over the best baseline. Remarkably, it also reduces FLOPs by 42.5% rel- ative to U-Net++ while maintaining higher accuracy. These results demonstrate that, by combining multi-scale attention with a learned smoothing prior, UAMSA-UNet achieves both better segmentation quality and computational efficiency, and provides a flexible foundation for future integration with transformer-based modules for further enhanced segmenta- tion results.