ArticleIEEE transactions on pattern analysis and machine intelligence2024
Mine Your Own Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels.
Article in IEEE transactions on pattern analysis and machine intelligence, 2024. 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.
- Semi-supervised Medical Image Segmentation Using Heterogeneous Complementary Correction Network and Confidence Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- Catheter detection and segmentation in X-ray images via multi-task learning.International journal of computer assisted radiology and surgery · 2026Article
- 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
- Diffusion-driven distillation and contrastive learning for class-incremental semantic segmentation of laparoscopic images.International journal of computer assisted radiology and surgery · 2025Article
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- Structured hashing with deep learning for modality, organ, and disease content sensitive medical image retrieval.Scientific reports · 2025Article
- A deep ensemble learning framework for glioma segmentation and grading prediction.Scientific reports · 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
- Lightweight Super-Resolution Techniques in Medical Imaging: Bridging Quality and Computational Efficiency.Bioengineering (Basel, Switzerland) · 2024Article
- Multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation from CT scans.Scientific reports · 2024Article
- DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images.Medical image analysis · 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
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9 authors.
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Abstract
Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping (i.e., pulling positive samples closer and negative samples apart in the feature space). However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owNAnatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances-through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, we both empirically and theoretically, demonstrate the efficacy of our MONA on three benchmark datasets with different labeled settings, achieving new state-of-the-art under different labeled semi-supervised settings. MONA makes minimal assumptions on domain expertise, and hence constitutes a practical and versatile solution in medical image analysis. We provide the PyTorch-like pseudo-code in supplementary.
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