DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification
Journal:
arXiv
Published Date:
May 29, 2025
Abstract
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
