Semi-supervised Medical Image Classification
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Benchmarks
Chest X-Ray14 2% labeled
Most implemented
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Universal Semi-Supervised Learning for Medical Image Classification
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
Semi-supervised Medical Image Classification with Global Latent Mixing
Papers
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+1