  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.
