Heatmap Regression for Lesion Detection using Pointwise Annotations

• URL: http://arxiv.org/abs/2208.05939v1
• Date: Thu, 11 Aug 2022 17:26:09 GMT
• Title: Heatmap Regression for Lesion Detection using Pointwise Annotations
• Authors: Chelsea Myers-Colet, Julien Schroeter, Douglas L. Arnold, Tal Arbel
• Abstract summary: 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. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels.
• Score: 3.6513059119482145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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