# One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation

## TL;DR

This paper introduces OM-Net, a lightweight multi-task network with cross-task guided attention for brain tumor segmentation, achieving state-of-the-art results with one-pass computation and improved class imbalance handling.

## Contribution

The paper proposes a novel one-pass multi-task network with cross-task guided attention, integrating shared and task-specific features, and leveraging task correlation for improved brain tumor segmentation.

## Key findings

- Achieved state-of-the-art performance on BraTS 2015 and 2017 datasets.
- Won third place in BraTS 2018 challenge among 64 teams.
- Demonstrated effectiveness of cross-task guided attention and joint training strategies.

## Abstract

Class imbalance has emerged as one of the major challenges for medical image segmentation. The model cascade (MC) strategy significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation. Despite its outstanding performance, however, this method leads to undesired system complexity and also ignores the correlation among the models. To handle these flaws, we propose a light-weight deep model, i.e., the One-pass Multi-task Network (OM-Net) to solve class imbalance better than MC does, while requiring only one-pass computation. First, OM-Net integrates the separate segmentation tasks into one deep model, which consists of shared parameters to learn joint features, as well as task-specific parameters to learn discriminative features. Second, to more effectively optimize OM-Net, we take advantage of the correlation among tasks to design both an online training data transfer strategy and a curriculum learning-based training strategy. Third, we further propose sharing prediction results between tasks and design a cross-task guided attention (CGA) module which can adaptively recalibrate channel-wise feature responses based on the category-specific statistics. Finally, a simple yet effective post-processing method is introduced to refine the segmentation results. Extensive experiments are conducted to demonstrate the effectiveness of the proposed techniques. Most impressively, we achieve state-of-the-art performance on the BraTS 2015 testing set and BraTS 2017 online validation set. Using these proposed approaches, we also won joint third place in the BraTS 2018 challenge among 64 participating teams. The code is publicly available at https://github.com/chenhong-zhou/OM-Net.