mlsanityData to 7 Oct 2026

One-Shot Medical Landmark Detection

arXiv 2103.04527 ↗First posted 8 Mar 2021cs.CVcited by 31

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Abstract (arXiv)

The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images. To relieve such a burden for a landmark detection task, we explore the feasibility of using only a single annotated image and propose a novel framework named Cascade Comparing to Detect (CC2D) for one-shot landmark detection. CC2D consists of two stages: 1) Self-supervised learning (CC2D-SSL) and 2) Training with pseudo-labels (CC2D-TPL). CC2D-SSL captures the consistent anatomical information in a coarse-to-fine fashion by comparing the cascade feature representations and generates predictions on the training set. CC2D-TPL further improves the performance by training a new landmark detector with those predictions. The effectiveness of CC2D is evaluated on a widely-used public dataset of cephalometric landmark detection, which achieves a competitive detection accuracy of 81.01% within 4.0mm, comparable to the state-of-the-art fully-supervised methods using a lot more than one training image.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 19 of its 37 references that are in the map. First posted on arXiv 8 Mar 2021.

Builds on

19 of 37 references.

References · 19

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  5. 2017A Closer Look at Memorization in Deep Networkscited by 936
  6. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  7. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 230
  8. 2019Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysiscited by 159
  9. 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
  10. 2019Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localizationcited by 77
  11. 2020Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representationscited by 69
  12. 2020Structured Landmark Detection via Topology-Adapting Deep Graph Learningcited by 50
7 more
  1. 2019Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Votingcited by 32
  2. 2019An Attention-Guided Deep Regression Model for Landmark Detection in Cephalogramscited by 27
  3. 2018X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgerycited by 22
  4. 2020Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detectioncited by 21
  5. 2017Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularizationcited by 11
  6. 2020Self-Supervised Discovery of Anatomical Shape Landmarkscited by 7
  7. 2020Bounding Maps for Universal Lesion Detectioncited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 11; 2024: 4; 2025: 5; 2026: 1.

31 papers.

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  1. 2021Which images to label for few-shot medical landmark detection?cited by 16
  2. 2022Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detectioncited by 12
  3. 2023A Review of Predictive and Contrastive Self-supervised Learning for Medical Imagescited by 10
  4. 2024Cephalometric Landmark Detection across Ages with Prototypical Networkcited by 10
  5. 2023UOD: Universal One-shot Detection of Anatomical Landmarkscited by 9
  6. 2022DATR: Domain-adaptive transformer for multi-domain landmark detectioncited by 8
  7. 2022One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinementcited by 8
  8. 2024OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detectioncited by 8
  9. 2023Few-shot Geometry-Aware Keypoint Localizationcited by 6
  10. 2023Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Datasetcited by 6
  11. 2023Slide-SAM: Medical SAM Meets Sliding Windowcited by 6
  12. 2022Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detectioncited by 5
19 more
  1. 2023PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancementcited by 5
  2. 2023SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inferencecited by 5
  3. 2023Unsupervised Domain Adaptation for Anatomical Landmark Detectioncited by 5
  4. 2022Information-guided pixel augmentation for pixel-wise contrastive learningcited by 4
  5. 2023Towards multi-modal anatomical landmark detection for ultrasound-guided brain tumor resection with contrastive learningcited by 4
  6. 2024FM-OSD: Foundation Model-Enabled One-Shot Detection of Anatomical Landmarkscited by 4
  7. 2021Unsupervised Local Discrimination for Medical Imagescited by 3
  8. 2022Adversarial Focal Loss: Asking Your Discriminator for Hard Examplescited by 2
  9. 2023Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Imagescited by 2
  10. 2024Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray imagescited by 2
  11. 2021One-shot Weakly-Supervised Segmentation in Medical Imagescited by 1
  12. 2022MixCL: Pixel label matters to contrastive learningcited by 1
  13. 2025Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessmentcited by 1
  14. 2026Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Promptingcited by 1
  15. 2023Unsupervised augmentation optimization for few-shot medical image segmentationcited by 0
  16. 2025Landmarks Are Alike Yet Distinct: Harnessing Similarity and Individuality for One-Shot Medical Landmark Detectioncited by 0
  17. 2025Incremental Object Keypoint Learningcited by 0
  18. 2025BodyGPS: Anatomical Positioning Systemcited by 0
  19. 2025Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detectioncited by 0