提出无需梯度的动态任务加权方法，提升胸部X光多任务分类准确率
DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification
- 基于任务损失趋势自适应调整权重，不依赖梯度计算
- 在大规模X光数据集上实现7%的准确率提升，任务损失显著下降
- 训练速度提升3倍，适合医疗影像等关键场景快速部署
多任务学习（MTL）在医学影像等复杂领域具有共享表征的优势，但任务间贡献平衡仍是难题。本文提出DeepChest，一种针对多标签胸部X光（CXR）分类的高效动态任务加权框架。该方法不依赖梯度，通过分析任务损失趋势实现性能驱动的权重调整，模型无关且大幅降低内存占用，训练速度提升三倍。在大规模CXReye数据集上的实验表明，DeepChest相比现有最优MTL方法整体准确率提升7%，各任务损失显著减少，有效缓解负迁移并提升泛化能力。该方法可无缝集成至多种先进模型，为深度学习在医疗诊断中的实用部署提供支持。代码已开源。
原文摘要 · Abstract (English)
While Multi-Task Learning (MTL) offers inherent advantages in complex domains such as medical imaging by enabling shared representation learning, effectively balancing task contributions remains a significant challenge. This paper addresses this critical issue by introducing DeepChest, a novel, computationally efficient and effective dynamic task-weighting framework specifically designed for multi-label chest X-ray (CXR) classification. Unlike existing heuristic or gradient-based methods that often incur substantial overhead, DeepChest leverages a performance-driven weighting mechanism based on effective analysis of task-specific loss trends. Given a network architecture (e.g., ResNet18), our model-agnostic approach adaptively adjusts task importance without requiring gradient access, thereby significantly reducing memory usage and achieving a threefold increase in training speed. It can be easily applied to improve various state-of-the-art methods. Extensive experiments on a large-scale CXR dataset demonstrate that DeepChest not only outperforms state-of-the-art MTL methods by 7% in overall accuracy but also yields substantial reductions in individual task losses, indicating improved generalization and effective mitigation of negative transfer. The efficiency and performance gains of DeepChest pave the way for more practical and robust deployment of deep learning in critical medical diagnostic applications. The code is publicly available at https://github.com/youssefkhalil320/DeepChest-MTL
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