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.
@article{arxiv.2208.05939,
title = {Heatmap Regression for Lesion Detection using Pointwise Annotations},
author = {Chelsea Myers-Colet and Julien Schroeter and Douglas L. Arnold and Tal Arbel},
journal= {arXiv preprint arXiv:2208.05939},
year = {2022}
}
Comments
Accepted at Medical Image Learning with Limited & Noisy Data (MILLanD), a workshop hosted with the conference on Medical Image Computing and Computer Assisted Interventions (MICCAI) 2022