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arXiv · 2208.05939

Heatmap Regression for Lesion Detection using Pointwise Annotations

Chelsea Myers-Colet·Julien Schroeter·Douglas L. Arnold·Tal Arbel
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Abstract

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.

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Chelsea Myers-Colet, Julien Schroeter, Douglas L. Arnold, Tal Arbel. 2022-08-11. Heatmap Regression for Lesion Detection using Pointwise Annotations. https://arxiv.org/abs/2208.05939

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