DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification

• URL: http://arxiv.org/abs/2505.23595v1
• Date: Thu, 29 May 2025 16:08:26 GMT
• Title: DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification
• Authors: Youssef Mohamed, Noran Mohamed, Khaled Abouhashad, Feilong Tang, Sara Atito, Shoaib Jameel, Imran Razzak, Ahmed B. Zaky,
• Abstract summary: DeepChest is a dynamic task-weighting framework specifically designed for multi-label chest X-ray (CXR) classification.<n>Given a network architecture (e.g., ResNet18), our model-agnostic approach adaptively adjusts task importance without requiring gradient access.<n>Experiments on a large-scale CXR dataset demonstrate that DeepChest outperforms state-of-the-art MTL methods by 7% in overall accuracy.
• Score: 18.13819296102142
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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

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