TL;DR
This work investigates image-only, text-only, and multimodal learning on a curated subset of PadChest-GR, and demonstrates that simple concatenation-based fusion, when paired with appropriate balancing strategies and domain-specific encoders, provides a robust approach for imbalanced multi-label chest X-ray classification.
Overall, the proposed QMIX-ViT framework provides a structured approach for multi-class chest X-ray classification under heterogeneous data conditions and suggests that disease-group expert decomposition and monotonic fusion can reduce inter-class interference and improve decision-level stability.
Chest X-ray report generation systems are valuable for assisting disease diagnosis and improving healthcare efficiency. However, existing methods still face two key challenges. First, multiple diseases often co-occur, leading to a combinatorial explosion of label combinations and sparse supervision for learning a gener...
Accurate and interpretable multi-class recognition of lung diseases from chest x-ray (CXR) images remains challenging because different pulmonary conditions can present with overlapping radiographic patterns, making reliable automated diagnosis difficult in clinical screening and decision support. This study aims to de...
The results suggest that the hybrid CNN-Transformer model provides a strong level of diagnostic accuracy and meaningfully understood visual rationale so it can serve as an excellent decision support mechanism for hospitals and radiologists in their daily operations.
Adequate classification of pneumonia is essential for timely disease management and effective resource allocation in healthcare systems. As pneumonia presents with varying severity and its assessment utilizing chest radiographs is challenging, specifically detecting MILD and NORMAL-PCR+ cases, where chest radiographs m...
Chest X-ray imaging is widely used for examining abnormalities associated with the lungs and respiratory system. The increasing availability of medical image datasets has created opportunities for applying deep learning techniques to assist in the preliminary analysis of chest radiographs. However, classification of di...
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