Semi-supervised learning for medical image classification using imbalanced training data
Medical image classification is often challenging for two reasons: a lack of labelled examples due to expensive and time-consuming annotation protocols, and imbalanced class labels due to the relative scarcity of disease-positive individuals in the wider population. Semi-supervised learning (SSL) methods exist for dealing with a lack of labels, but they generally do not address the problem of class imbalance. In this study we propose Adaptive Blended Consistency Loss (ABCL), a drop-in replacement for consistency loss in perturbation-based SSL methods. ABCL counteracts data skew by adaptively mixing the target class distribution of the consistency loss in accordance with class frequency. Our experiments with ABCL reveal improvements to unweighted average recall on two different imbalanced medical image classification datasets when compared with existing consistency losses that are not designed to counteract class imbalance.
Code (0)
등록된 구현이 없습니다.
Tasks
image-classificationImage ClassificationMedical Image ClassificationSimilar Papers 제목 키워드 기반
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a small number of labeled samples from the …
ClusteringDomain Adaptationimage-classificationImage Classification+3Class-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 ClassificationDynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use. In this paper, w…
Federated LearningTowards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework
Deep learning in medical imaging is often limited by scarce and imbalanced annotated data. We present SSGNet, a unified framework that combines class specific generative modeling with iterative semisupervised pseudo labe…
ACPL: 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+3