# Heatmap Regression for Lesion Detection using Pointwise Annotations

arXiv: https://arxiv.org/abs/2208.05939 · HTML: https://mlsanity.com/paper/2208.05939/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 11 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://mlsanity.com/paper/1904.07399/) (2019)
- [Flowing ConvNets for Human Pose Estimation in Videos](https://mlsanity.com/paper/1506.02897/) (2015)
- [Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization](https://mlsanity.com/paper/2109.09533/) (2021)
- [Heatmap-based 2D Landmark Detection with a Varying Number of Landmarks](https://mlsanity.com/paper/2101.02737/) (2021)
- [Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network](https://mlsanity.com/paper/1907.12452/) (2019)
- [Point detection through multi-instance deep heatmap regression for sutures in endoscopy](https://mlsanity.com/paper/2111.08468/) (2021)
- [Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network](https://mlsanity.com/paper/1802.06865/) (2018)
- [Segmentation-Consistent Probabilistic Lesion Counting](https://mlsanity.com/paper/2204.05276/) (2022)

## Built on by (most cited first, 1 of 1)

- [ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images](https://mlsanity.com/paper/2404.13103/) (2024)

## Abstract (arXiv)

In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.