Distribution-aware probability contrastive learning for class-imbalanced semi-supervised learning

Debiased label enhancement for class-imbalanced semi-supervised learning

C-Score: Beyond Accuracy for Robustness Assessment in Semi-Supervised Learning under Open-World Unlabeled Contamination

C-Score, a compact framework that evaluates training behavior in three complementary spaces: prediction, feature representation, and optimization, suggests that clean accuracy alone is insufficient for evaluating SSL robustness in open-world environments, and that internal diagnostic signals are necessary for more reliable robustness assessment under unlabeled contamination.

A data-driven semi-supervised framework with adaptive quality filtering for imbalanced binary image classification

A Comprehensive Survey on Self-Supervised Learning on Computer Vision: Moving Beyond the Supervised Paradigm

The availability of large-scale labeled datasets has driven advances in AI-based computer vision, yet supervised learning remains costly and impractical in domains where annotation is scarce. Self-supervised learning (SSL) addresses this by harnessing unlabeled data to learn rich, transferable representations without explicit supervision. This survey presents a seven-category taxonomy of self-supervised learning methods covering: 1) input reconstruction or restoration, 2) context prediction, 3) contrastive learning, 4) feature clustering, 5) self-distillation-based feature reconstruction, 6) redundancy reduction, and 7) masked image modeling, with coverage extended to recent methods that include DINOv2, I-JEPA, SparK, data2vec 2.0, V-JEPA, DINOv3, V-JEPA 2, V-JEPA 2.1, C-JEPA, PhiNet v2. We situate this work within the existing survey landscape by explicitly comparing our contributions with prior SSL reviews. Beyond method descriptions, we provide: a chronological timeline of SSL evolution from 2008 to 2026; a cross-paradigm comparative analysis evaluating all seven families along collapse risk, scalability, computational cost, and downstream transferability; a dedicated comparative analysis of anti-collapse mechanisms; critical limitations and trade-off analyses per method family; and systematic benchmarking evidence on ImageNet-1K, PASCAL VOC, COCO, and five public medical imaging datasets. We also contribute a practical method selection decision matrix, extended challenge discussions, and actionable open problems for future research.

Recurrent Contrastive Learning for Imbalanced Medical Image Classification

Medical image classification often suffers from class imbalance due to the inherent disparities in disease incidence. Existing approaches, such as class resampling and loss reweighting, mainly improve learning within the observed feature distribution, but do not explicitly enlarge the latent support region of tail classes. As a result, tail-class representations remain overly compact and are easily encroached upon by head classes, leading to biased decision boundaries. In this work, we propose Recurrent Contrastive Learning (RCL) for imbalanced medical image classification. RCL progressively expands the support region of tail classes by recurrently reusing historical feature states across training phases. Specifically, we adopt DINOv3 with LoRA adapters as the backbone to provide robust feature embeddings. We then devise a Temporal Memory Queue (TMQ) to preserve corpus-level features across training phases and provide diversified global references for contrastive learning. Based on TMQ, we construct Temporal Anchors (TARs) to form an anchor field around tail classes. This field enlarges the support region of tail classes, suppresses head-class encroachment, and improves inter-class separation. Extensive experiments on three imbalanced medical datasets demonstrate that RCL achieves consistent improvements over strong baselines. The code is available at https://github.com/dndins/RCL.

Imbalance-Aware Robust Representation Learning for Medical Image Binary Classification

Class imbalance is a prevalent issue in medical image classification that significantly degrades a model's capacity to recognize minority-class lesions, thereby restricting its applicability in real-world clinical screening scenarios. Existing studies typically address this problem through data resampling, loss re-weighting, or decision boundary adjustment strategies; however, these methods predominantly focus on compensation during the classification stage. In contrast, the representation learning process in earlier stages is often dominated by easy majority-class samples, and its impact on the feature quality of minority classes has not received adequate attention. To address this issue, we propose an Imbalance-Aware Robust Representation Learning (IRRL) framework for class-imbalanced medical image classification. IRRL prioritizes the refinement of minority-class-related local representations before global classification. Specifically, implicit local token representations are constructed from convolutional feature maps based on their receptive-field structure. Semantic confidence-guided reliability estimation, difficulty-adaptive supervised contrastive learning, and minority-class prototype regularization are then introduced to improve the learning of informative local representations and hard minority-class samples. Finally, a Transformer performs global context modeling for image-level classification. Experiments on four public datasets, including ISIC 2018, PAD-UFES-20, OCTID, and BUSI, show that IRRL achieves balanced classification performance, with favorable F1-score and Matthews Correlation Coefficient (MCC) results that reflect improved minority-class recognition quality. The results across datasets with different imaging modalities and imbalance conditions further demonstrate the robustness and consistency of the proposed representation learning strategy.