Papers X-ray Classification
“X-ray Classification” 태그가 달린 논문 70편 · 필터 해제
Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference
Medical imaging data contain sensitive patient information requiring strong privacy protection. Many analytical setups require data to be sent to a server for inference purposes. Homomorphic encryption (HE) provides a so…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPrivacy PreservingX-ray ClassificationDeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification
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. …
DiagnosticMulti-Task LearningRepresentation LearningX-ray ClassificationAutomated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and is one of the leading causes of mortality in children. Globally, lung-re…
Binary ClassificationClassificationDiagnosticMulti-class Classification+2MedCAM-OsteoCls: Medical Context Aware Multimodal Classification of Knee Osteoarthritis
Knee Osteoarthritis (KOA) is a degenerative musculoskeletal joint disorder that significantly impacts middle-aged and elderly individuals. Although X-rays and MRIs are clinically used to identify such disorders, combinin…
AnatomyClassificationDiagnosticMRI classification+1Enhancing Chest X-ray Classification through Knowledge Injection in Cross-Modality Learning
The integration of artificial intelligence in medical imaging has shown tremendous potential, yet the relationship between pre-trained knowledge and performance in cross-modality learning remains unclear. This study inve…
Caption GenerationClassificationDiagnosticMedical Image Analysis+3Improving the Efficiency of Self-Supervised Adversarial Training through Latent Clustering-Based Selection
Compared with standard learning, adversarially robust learning is widely recognized to demand significantly more training examples. Recent works propose the use of self-supervised adversarial training (SSAT) with externa…
ClusteringX-ray ClassificationReal-time Chest X-Ray Distributed Decision Support for Resource-constrained Clinics
Internet of Things (IoT) based healthcare systems offer significant potential for improving the delivery of healthcare services in humanitarian engineering, providing essential healthcare services to millions of underser…
Humanitarianimage-classificationImage ClassificationX-ray ClassificationLung Disease Detection with Vision Transformers: A Comparative Study of Machine Learning Methods
Recent advancements in medical image analysis have predominantly relied on Convolutional Neural Networks (CNNs), achieving impressive performance in chest X-ray classification tasks, such as the 92% AUC reported by AutoT…
DiagnosticMedical Image AnalysisX-ray ClassificationRE-tune: Incremental Fine Tuning of Biomedical Vision-Language Models for Multi-label Chest X-ray Classification
In this paper we introduce RE-tune, a novel approach for fine-tuning pre-trained Multimodal Biomedical Vision-Language models (VLMs) in Incremental Learning scenarios for multi-label chest disease diagnosis. RE-tune free…
Computational EfficiencyIncremental LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
Federated Learning (FL) is a rising approach towards collaborative and privacy-preserving machine learning where large-scale medical datasets remain localized to each client. However, the issue of data heterogeneity amon…
Federated LearningPrivacy PreservingX-ray ClassificationDomain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTransfer Learning+1BiasPruner: Debiased Continual Learning for Medical Image Classification
Continual Learning (CL) is crucial for enabling networks to dynamically adapt as they learn new tasks sequentially, accommodating new data and classes without catastrophic forgetting. Diverging from conventional perspect…
ClassificationContinual LearningFairnessimage-classification+5Low-Resolution Chest X-ray Classification via Knowledge Distillation and Multi-task Learning
This research addresses the challenges of diagnosing chest X-rays (CXRs) at low resolutions, a common limitation in resource-constrained healthcare settings. High-resolution CXR imaging is crucial for identifying small b…
DiagnosticKnowledge DistillationMulti-Task LearningX-ray ClassificationExpanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification
Quantum machine learning (QML) has the potential for improving the multi-label classification of rare, albeit critical, diseases in large-scale chest x-ray (CXR) datasets due to theoretical quantum advantages over classi…
Binary ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
There has been significant progress in implementing deep learning models in disease diagnosis using chest X- rays. Despite these advancements, inherent biases in these models can lead to disparities in prediction accurac…
DiagnosticFairnessMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Human Expertise in Algorithmic Prediction
We introduce a novel framework for incorporating human expertise into algorithmic predictions. Our approach leverages human judgment to distinguish inputs which are algorithmically indistinguishable, or "look the same" t…
PredictionX-ray ClassificationA Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification
Image classifiers for domain-specific tasks like Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) and chest X-ray classification often rely on convolutional neural networks (CNNs). These networks, while po…
AllClassificationimage-classificationImage Classification+2Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study
Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. However, some challenges are associated with these datasets, including variability in d…
Data AugmentationFew-Shot LearningTransfer LearningX-ray ClassificationUniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification
Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in …
X-ray ClassificationLT-ViT: A Vision Transformer for multi-label Chest X-ray classification
Vision Transformers (ViTs) are widely adopted in medical imaging tasks, and some existing efforts have been directed towards vision-language training for Chest X-rays (CXRs). However, we envision that there still exists …
X-ray Classification