# Salt & Pepper Heatmaps: Diffusion-informed Landmark Detection Strategy

Source: [Salt & Pepper Heatmaps: Diffusion-informed Landmark Detection Strategy](<https://www.sota2.com/research/paper/68fa740e1e209131cea5f83f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:01:03.352Z

Authors: Julian Wyatt, Irina Voiculescu

Published: 2024-07-12T11:50:39.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2407.09192>)

[PDF](<https://arxiv.org/pdf/2407.09192.pdf>)