ArticleInformation processing in medical imaging : proceedings of the ... conference2023
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-Aware Contrastive Distillation.
Article in Information processing in medical imaging : proceedings of the ... conference, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- ProtoSAM for automated one shot medical image segmentation using foundational models.Scientific reports · 2025Article
- Comparative analysis of supervised and self-supervised learning with small and imbalanced medical imaging datasets.Scientific reports · 2025Article
- A highly generalized federated learning algorithm for brain tumor segmentation.Scientific reports · 2025Article
- Improved SwinUNet with fusion transformer and large kernel convolutional attention for liver and tumor segmentation in CT images.Scientific reports · 2025Article
- A deep ensemble learning framework for glioma segmentation and grading prediction.Scientific reports · 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
- 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
- Survival prediction and molecular subtyping of squamous cell lung cancer based on network embedding.Scientific reports · 2024Article
- Multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation from CT scans.Scientific reports · 2024Article
- Article
- Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI).Quantitative imaging in medicine and surgery · 2024Article
- DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images.Medical image analysis · 2024Article
- Exploring Unlabeled Data in Multiple Aspects for Semi-Supervised MRI Segmentation.Health data science · 2024Article
- Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective.Advances in neural information processing systems · 2023Article
- ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Improve the performance of CT-based pneumonia classification via source data reweighting.Scientific reports · 2023Article
- Dense regression activation maps for lesion segmentation in CT scans of COVID-19 patients.Medical image analysis · 2023Article
- Class-Aware Adversarial Transformers for Medical Image Segmentation.Advances in neural information processing systems · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images. However, medical image data in reality is commonly imbalanced (
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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.