ArticleAdvances in neural information processing systems2023
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective.
Article in Advances in neural information processing systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- NFSAOA: a new approach of medical brain image fusion using neutrosophic fuzzy set and arithmetic optimization algorithm.Medical & biological engineering & computing · 2026Article
- Emotion correction for weibo users via weakly supervised learning and semantic understanding.iScience · 2026Article
- Efficient Medical Image Segmentation in Multisensor Imaging: A Survey in the Era of Mamba and Foundation Models.Sensors (Basel, Switzerland) · 2026Review
- FFTMed: leveraging fast-fourier transform for a lightweight and adversarial-resilient medical image segmentation framework.Scientific reports · 2025Article
- Comparative analysis of supervised and self-supervised learning with small and imbalanced medical imaging datasets.Scientific reports · 2025Article
- Adaptive sensitivity-fisher regularization for heterogeneous transfer learning of vascular segmentation in laparoscopic videos.International journal of computer assisted radiology and surgery · 2025Article
- Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.Tomography (Ann Arbor, Mich.) · 2025Review
- Improved SwinUNet with fusion transformer and large kernel convolutional attention for liver and tumor segmentation in CT images.Scientific reports · 2025Article
- Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis.... International Conference on Learning Representations · 2025Article
- A deep ensemble learning framework for glioma segmentation and grading prediction.Scientific reports · 2025Article
- MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation.Frontiers in medicine · 2025Article
- Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference.IEEE transactions on medical imaging · 2025Article
- Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025Article
- Interdisciplinary approaches to image processing for medical robotics.Frontiers in medicine · 2025Article
- Computed tomography-derived radiomics models for distinguishing difficult-to-diagnose inflammatory and malignant pulmonary nodules.Biomedical engineering and computational biology · 2025Article
- Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network.BMC medical informatics and decision making · 2024Article
- A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides.Communications biology · 2024Article
- Enhanced Cross-stage-attention U-Net for esophageal target volume segmentation.BMC medical imaging · 2024Article
- Robust thoracic CT image registration with environmental adaptability using dynamic Welsch's function and hierarchical structure-awareness strategy.Quantitative imaging in medicine and surgery · 2024Article
- Task-driven framework using large models for digital pathology.Communications biology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth labels, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical regions and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose ARCO, a semi-supervised contrastive learning (CL) framework with stratified group theory for medical image segmentation. In particular, we first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on eight benchmarks,
Identifiers
38813114PMC11136570What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.