MT-IDS: A multi-task information decoupling strategy for identifying lymph node metastasis in the mediastinal region.

Accurately identifying the mediastinal regions where metastatic lymph nodes are located is critical for the staging diagnosis of lung cancer. This identification task involves two distinct detection dimensions: mediastinal region identification and lymph node metastasis assessment. Traditional single-task image classification algorithms struggle to manage the interference between different classification dimensions within a single task. Existing multi-task learning methods struggle to balance the relationship between shared and task-specific features, and often fail to effectively fit the underlying data distributions and task characteristics during gradient adjustment. To address these challenges, we propose a Multi-Task Information Decoupling Strategy (MT-IDS). MT-IDS decomposes the main task into multiple auxiliary tasks along different feature dimensions, forming a unified multi-task system to optimize detection performance across tasks. A Dual-control Branch Routing Gate Mechanism (DBR) is employed in MT-IDS to compute the weighting of shared and task-specific features, thereby enabling more precise expert selection and feature extraction for each task. Additionally, a Dual-Dimensional Gradient Balancing Algorithm (DD-GB) is introduced in MT-IDS, whereby gradient balance is achieved through alignment of gradient directions and dynamic scaling of magnitudes, while the distribution of inter-task gradient characteristics is maintained. The significant advantages demonstrated by MT-IDS in both ablation and comparative experiments indicate its potential as an innovative solution for multi-dimensional medical image classification problems.