Papers Semi-supervised Medical Image Classification
“Semi-supervised Medical Image Classification” 태그가 달린 논문 14편 · 필터 해제
Multimodal Medical Image Classification via Synergistic Learning Pre-training
Multimodal pathological images are usually in clinical diagnosis, but computer vision-based multimodal image-assisted diagnosis faces challenges with modality fusion, especially in the absence of expert-annotated data. T…
Semi-supervised Medical Image ClassificationSelf-Supervised LearningJudge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification
Semi-supervised learning (SSL) is a popular solution to alleviate the high annotation cost in medical image classification. As a main branch of SSL, consistency regularization engages in imposing consensus between the pr…
Contrastive Learningimage-classificationImage ClassificationMedical Image Classification+1Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification
Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of …
image-classificationImage ClassificationMedical Image ClassificationSemi-supervised Medical Image ClassificationSPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification
Medical image classification is a challenging task due to the scarcity of labeled samples and class imbalance caused by the high variance in disease prevalence. Semi-supervised learning (SSL) methods can mitigate these c…
Classificationimage-classificationImage ClassificationLesion Classification+3Universal Semi-Supervised Learning for Medical Image Classification
Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built …
ClassificationDomain Adaptationimage-classificationImage Classification+2Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification
Intelligent medical diagnosis has shown remarkable progress based on the large-scale datasets with precise annotations. However, fewer labeled images are available due to significantly expensive cost for annotating data …
image-classificationImage ClassificationMedical DiagnosisMedical Image Classification+1PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training
Pseudo-labeling approaches have been proven beneficial for semi-supervised learning (SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding samples with high-confidence pseudo-labels…
image-classificationImage ClassificationMedical Image ClassificationSemi-supervised Medical Image ClassificationACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosi…
image-classificationImage ClassificationMedical Image AnalysisMedical Image Classification+3Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms only allow the supervised training settin…
Federated Learningimage-classificationImage ClassificationMedical Image Classification+2Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
The training of deep learning models generally requires a large amount of annotated data for effective convergence and generalisation. However, obtaining high-quality annotations is a laboursome and expensive process due…
Contrastive LearningGeneral ClassificationMedical Image AnalysisMulti-class Classification+4In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely on domain-specific data augmentations, wh…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPseudo LabelSemi-Supervised Image Classification+2Semi-supervised Medical Image Classification with Global Latent Mixing
Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled d…
ClassificationGeneral Classificationimage-classificationImage Classification+2Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accu…
ClassificationGeneral Classificationimage-classificationImage Classification+5GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classif…
ClassificationGeneral ClassificationMulti-class ClassificationSemi-supervised Medical Image Classification+1