ArticleIEEE transactions on medical imaging2021
Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Exploring anatomical similarity in zero-shot learning for bone abnormality detection.Scientific reports · 2026Article
- Deep learning with noisy labels in medical prediction problems: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Severity of error in hierarchical datasets.Scientific reports · 2023Article
- Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning.Nature biomedical engineering · 2022Article
- Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study.Data augmentation, labelling, and imperfections : second MICCAI workshop, DALI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. DALI (Workshop) (2nd : 2022 : Singapore) · 2022Article
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- Prime Time for Artificial Intelligence in Interventional Radiology.Cardiovascular and interventional radiology · 2022Review
- Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data.Frontiers in physiology · 2022Review
- Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities.PET clinics · 2022Review
- Training Strategies for Radiology Deep Learning Models in Data-limited Scenarios.Radiology. Artificial intelligence · 2021Review
- Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging.IEEE transactions on medical imaging · 2021Article
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Authors and funding
7 authors.
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Abstract
Zero-shot learning (ZSL) is one of the most promising avenues of annotation-efficient machine learning. In the era of deep learning, ZSL techniques have achieved unprecedented success. However, the developments of ZSL methods have taken place mostly for natural images. ZSL for medical images has remained largely unexplored. We design a novel strategy for generalized zero-shot diagnosis of chest radiographs. In doing so, we leverage the potential of multi-view semantic embedding, a useful yet less-explored direction for ZSL. Our design also incorporates a self-training phase to tackle the problem of noisy labels alongside improving the performance for classes not seen during training. Through rigorous experiments, we show that our model trained on one dataset can produce consistent performance across test datasets from different sources including those with very different quality. Comparisons with a number of state-of-the-art techniques show the superiority of the proposed method for generalized zero-shot chest x-ray diagnosis.
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