Multi-task learning (MTL) has shown great potential in medical image analysis, improving the generalizability of the learned features and the performance in individual tasks. However, most of the work on MTL focuses on either architecture design or gradient manipulation, while in both scenarios, features are learned in a competitive manner. In this work, we propose to formulate MTL as a multi/bi-level optimization problem, and therefore force features to learn from each task in a cooperative approach. Specifically, we update the sub-model for each task alternatively taking advantage of the learned sub-models of the other tasks. To alleviate the negative transfer problem during the optimization, we search for flat minima for the current objective function with regard to features from other tasks. To demonstrate the effectiveness of the proposed approach, we validate our method on three publicly available datasets. The proposed method shows the advantage of cooperative learning, and yields promising results when compared with the state-of-the-art MTL approaches. The code will be available online.

翻译：多任务学习（MTL）在医学图像分析中展现出巨大潜力，能够提升所学特征的泛化能力及各独立任务的性能。然而，现有MTL研究主要聚焦于架构设计或梯度调控，在这两种场景中，特征均以竞争方式学习。本文提出将MTL建模为多层/双层优化问题，从而促使特征以协同方式从各任务中学习。具体而言，我们利用其他任务已学习的子模型，交替更新每个任务的子模型。为缓解优化过程中的负迁移问题，我们针对当前目标函数搜索关于其他任务特征的平坦最小值。为验证所提方法的有效性，我们在三个公开数据集上进行了实验验证。结果表明，该方法能够实现协同学习优势，与当前最优MTL方法相比取得了具有竞争力的性能。相关代码将在网上公开。