  Automatic lesion segmentation on thoracic CT enables rapid quantitative
analysis of lung involvement in COVID-19 infections. However, obtaining a large
amount of voxel-level annotations for training segmentation networks is
prohibitively expensive. Therefore, we propose a weakly-supervised segmentation
method based on dense regression activation maps (dRAMs). Most
weakly-supervised segmentation approaches exploit class activation maps (CAMs)
to localize objects. However, because CAMs were trained for classification,
they do not align precisely with the object segmentations. Instead, we produce
high-resolution activation maps using dense features from a segmentation
network that was trained to estimate a per-lobe lesion percentage. In this way,
the network can exploit knowledge regarding the required lesion volume. In
addition, we propose an attention neural network module to refine dRAMs,
optimized together with the main regression task. We evaluated our algorithm on
90 subjects. Results show our method achieved 70.2% Dice coefficient,
substantially outperforming the CAM-based baseline at 48.6%.
