mlsanityData to 7 Oct 2026

Salt & Pepper Heatmaps: Diffusion-informed Landmark Detection Strategy

arXiv 2407.09192 ↗First posted 12 Jul 2024cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Anatomical Landmark Detection is the process of identifying key areas of an image for clinical measurements. Each landmark is a single ground truth point labelled by a clinician. A machine learning model predicts the locus of a landmark as a probability region represented by a heatmap. Diffusion models have increased in popularity for generative modelling due to their high quality sampling and mode coverage, leading to their adoption in medical image processing for semantic segmentation. Diffusion modelling can be further adapted to learn a distribution over landmarks. The stochastic nature of diffusion models captures fluctuations in the landmark prediction, which we leverage by blurring into meaningful probability regions. In this paper, we reformulate automatic Anatomical Landmark Detection as a precise generative modelling task, producing a few-hot pixel heatmap. Our method achieves state-of-the-art MRE and comparable SDR performance with existing work.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 16 of its 28 references that are in the map. First posted on arXiv 12 Jul 2024.

Builds on

16 of 28 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec

Other references · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2013Auto-Encoding Variational Bayescited by 3,980
  7. 2016Wide Residual Networkscited by 3,057
  8. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  9. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
  10. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  11. 2018Facial Landmark Detection: a Literature Surveycited by 63
  12. 2019Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Votingcited by 32
3 more
  1. 2019An Attention-Guided Deep Regression Model for Landmark Detection in Cephalogramscited by 27
  2. 2024Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?cited by 4
  3. 2023Uncertainty-aware U-Net for Medical Landmark Detectioncited by 2

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  1. 2024Synthetic Augmentation for Anatomical Landmark Localization using DDPMs
  2. 2024Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images
  3. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey
  4. 2025Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models
  5. 2025Medical Semantic Segmentation with Diffusion Pretrain
  6. 2023Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models
  7. 2024MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications
  8. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review