Thoracic Disease Classification
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Benchmarks
ChestX-ray14
Most implemented
Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection from Chest X-rays
Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images
Papers
From Classification to Localization and Clinical Validation: Large-Scale Development of a Deep Learning System for Thoracic Disease Detection on Chest Radiographs in Thailand
Chest radiography (CXR) remains the most widely used thoracic imaging modality, yet expert interpretation is constrained by a severe shortage of radiologists in Thailand and across Southeast Asia. Local adaptation of dee…
Thoracic Disease ClassificationPulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classification
Automated chest X-ray classification remains challenging due to severe class imbalance, co-occurring pathologies, and the loss of localized features in conventional architectures. To address these, we propose an explaina…
Thoracic Disease ClassificationAdURA-Net: Adaptive Uncertainty and Region-Aware Network
One of the common issues in clinical decision-making is the presence of uncertainty, which often arises due to ambiguity in radiology reports, which often reflect genuine diagnostic uncertainty or limitations of automate…
Thoracic Disease ClassificationArtificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images
Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical structures to perform disease diagnosis. An automatic multi-label classifier of diseases in CXR images can benefit by incorp…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONScanpath predictionThoracic Disease ClassificationOptimizing CNN Architectures for Advanced Thoracic Disease Classification
Machine learning, particularly convolutional neural networks (CNNs), has shown promise in medical image analysis, especially for thoracic disease detection using chest X-ray images. In this study, we evaluate various CNN…
Binary ClassificationClassificationImage CompressionMedical Image Analysis+3SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification
Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early …
Deep LearningMulti-Label ClassificationThoracic Disease ClassificationX-ray Classification