Adaptive auxiliary task learning for medical image segmentation under scarce training data

Medical image segmentation often suffers from scarce training data, which limits the performance of deep learning models. Auxiliary task learning offers a promising solution by exploiting complementary supervision signals constructed solely from existing data. However, auxiliary tasks may introduce complex learning signals, and naive integration of multiple tasks often leads to limited performance gains while drastically increasing computational cost. This study presents an adaptive auxiliary task learning method to improve medical image segmentation under scarce training data. The method introduces an adaptive knowledge transfer mechanism that aggregates multi-step model updates from different tasks and model components using learnable weights, enabling more expressive knowledge transfer than prior approaches. To improve training efficiency, a task selection strategy is developed to dynamically select a small subset of beneficial tasks for each training phase. By reusing intermediate computations, it enables the evaluation of task combinations with little overhead. The proposed method is implemented using eight auxiliary tasks with diverse properties. Experiments on five public datasets demonstrate consistent improvements over existing methods, surpassing the best competing approaches by 1.47%, 0.91%, 0.46%, 0.35%, and 2.44% in Dice score. Code is available at: https://github.com/BinyanHu/AdaAuxMedSeg.