  Coronavirus Disease 2019 (COVID-19) has spread aggressively across the world
causing an existential health crisis. Thus, having a system that automatically
detects COVID-19 in tomography (CT) images can assist in quantifying the
severity of the illness. Unfortunately, labelling chest CT scans requires
significant domain expertise, time, and effort. We address these labelling
challenges by only requiring point annotations, a single pixel for each
infected region on a CT image. This labeling scheme allows annotators to label
a pixel in a likely infected region, only taking 1-3 seconds, as opposed to
10-15 seconds to segment a region. Conventionally, segmentation models train on
point-level annotations using the cross-entropy loss function on these labels.
However, these models often suffer from low precision. Thus, we propose a
consistency-based (CB) loss function that encourages the output predictions to
be consistent with spatial transformations of the input images. The experiments
on 3 open-source COVID-19 datasets show that this loss function yields
significant improvement over conventional point-level loss functions and almost
matches the performance of models trained with full supervision with much less
human effort. Code is available at:
\url{https://github.com/IssamLaradji/covid19_weak_supervision}.
