Papers Thoracic Disease Classification
“Thoracic Disease Classification” 태그가 달린 논문 19편 · 필터 해제
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 ClassificationLearning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection from Chest X-rays
Performance degradation due to distribution discrepancy is a longstanding challenge in intelligent imaging, particularly for chest X-rays (CXRs). Recent studies have demonstrated that CNNs are biased toward styles (e.g.,…
Domain AdaptationDomain GeneralizationThoracic Disease ClassificationThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification
Objective: Computer-aided disease diagnosis and prognosis based on medical images is a rapidly emerging field. Many Convolutional Neural Network (CNN) architectures have been developed by researchers for disease classifi…
AnatomyClassificationPrognosisThoracic Disease ClassificationLearning Underrepresented Classes from Decentralized Partially Labeled Medical Images
Using decentralized data for federated training is one promising emerging research direction for alleviating data scarcity in the medical domain. However, in contrast to large-scale fully labeled data commonly seen in ge…
Federated LearningObject RecognitionSelf-Supervised LearningThoracic Disease ClassificationBreaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from t…
Open Set LearningThoracic Disease ClassificationAnatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays
Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identify…
AnatomyThoracic Disease ClassificationWeighing Features of Lung and Heart Regions for Thoracic Disease Classification
Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay …
BinarizationThoracic Disease ClassificationRethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study
Two DL models were developed using radiograph-level annotations (yes or no disease) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet and CheXDet. The models' internal classifi…
ClassificationDecision MakingGeneral ClassificationLesion Detection+1Multi-label Thoracic Disease Image Classification with Cross-Attention Networks
Automated disease classification of radiology images has been emerging as a promising technique to support clinical diagnosis and treatment planning. Unlike generic image classification tasks, a real-world radiology imag…
ClassificationGeneral Classificationimage-classificationImage Classification+1Deep Mining External Imperfect Data for Chest X-ray Disease Screening
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large distribution. Therefore, it is substantial …
General ClassificationMissing LabelsThoracic Disease ClassificationJointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain
Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain. Generally, very high dimensional medical images are down-sampled by us- ing interpolation techniq…
ClassificationGeneral ClassificationPneumonia DetectionThoracic Disease ClassificationSDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images
This study aims to automatically diagnose thoracic diseases depicted on the chest x-ray (CXR) images using deep convolutional neural networks. The existing methods generally used the entire CXR images for training purpos…
General ClassificationThoracic Disease ClassificationDynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization
We present and evaluate a new deep neural network architecture for automatic thoracic disease detection on chest X-rays. Deep neural networks have shown great success in a plethora of visual recognition tasks such as ima…
General Classificationimage-classificationImage Classificationobject-detection+2Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays
Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, du…
General ClassificationThoracic Disease Classification