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

arXiv: https://arxiv.org/abs/2108.08956 · HTML: https://mlsanity.com/paper/2108.08956/

## Key facts

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

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1605.01397/) (2016)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/1903.00348/) (2019)
- [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks](https://mlsanity.com/paper/1511.06390/) (2015)
- [Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation](https://mlsanity.com/paper/2006.16806/) (2020)
- [Class-Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2002.06815/) (2020)
- [Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach](https://mlsanity.com/paper/1808.10858/) (2018)
- [Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms](https://mlsanity.com/paper/1810.10348/) (2018)
- [Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks](https://mlsanity.com/paper/1703.04197/) (2017)
- [DNN and CNN with Weighted and Multi-task Loss Functions for Audio Event Detection](https://mlsanity.com/paper/1708.03211/) (2017)
- [Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data](https://mlsanity.com/paper/1801.00693/) (2018)

## Built on by (most cited first, 14 of 14)

- [Semi-supervised Concept Bottleneck Models](https://mlsanity.com/paper/2406.18992/) (2024)
- [CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets](https://mlsanity.com/paper/2307.13842/) (2023)
- [Efficient Medical Image Retrieval Using DenseNet and FAISS for BIRADS Classification](https://mlsanity.com/paper/2411.01473/) (2024)
- [Recent Advances in Medical Image Classification](https://mlsanity.com/paper/2506.04129/) (2025)
- [See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosis](https://mlsanity.com/paper/2506.18140/) (2025)
- [Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpox](https://mlsanity.com/paper/2303.09780/) (2023)
- [Taming Overconfident Prediction on Unlabeled Data from Hindsight](https://mlsanity.com/paper/2112.08200/) (2021)
- [Pelvic floor MRI segmentation based on semi-supervised deep learning](https://mlsanity.com/paper/2311.03105/) (2023)
- [Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition](https://mlsanity.com/paper/2409.13409/) (2024)
- [Optimal Policy Adaptation under Covariate Shift](https://mlsanity.com/paper/2501.08067/) (2025)
- [Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Images](https://mlsanity.com/paper/2511.13353/) (2025)
- [GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences](https://mlsanity.com/paper/2605.23183/) (2026)
- [An explainable hierarchical self attention-based approach for tremor detection in the time domain](https://mlsanity.com/paper/2606.00461/) (2026)
- [MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesis](https://mlsanity.com/paper/2609.04804/) (2026)

## 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.