mlsanityData to 7 Oct 2026

Semi-supervised learning for medical image classification using imbalanced training data

arXiv 2108.08956 ↗First posted 20 Aug 2021cs.CV cs.LGcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 20 Aug 2021.

Builds on

20 of 48 references.

References · 20

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8 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 3; 2025: 4; 2026: 3.

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  1. 2024Semi-supervised Concept Bottleneck Modelscited by 21
  2. 2023CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasetscited by 5
  3. 2024Efficient Medical Image Retrieval Using DenseNet and FAISS for BIRADS Classificationcited by 3
  4. 2025Recent Advances in Medical Image Classificationcited by 2
  5. 2025See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosiscited by 2
  6. 2023Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpoxcited by 1
  7. 2021Taming Overconfident Prediction on Unlabeled Data from Hindsightcited by 0
  8. 2023Pelvic floor MRI segmentation based on semi-supervised deep learningcited by 0
  9. 2024Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognitioncited by 0
  10. 2025Optimal Policy Adaptation under Covariate Shiftcited by 0
  11. 2025Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Imagescited by 0
  12. 2026GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequencescited by 0
2 more
  1. 2026An explainable hierarchical self attention-based approach for tremor detection in the time domaincited by 0
  2. 2026MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesiscited by 0