  Image-text contrastive learning has proven effective for pretraining medical
image models. When targeting localized downstream tasks like semantic
segmentation or object detection, additional local contrastive losses that
align image regions with sentences have shown promising results. We study how
local contrastive losses are related to global (per-sample) contrastive losses
and which effects they have on localized medical downstream tasks. Based on a
theoretical comparison, we propose to remove some components of local losses
and replace others by a novel distribution prior which enforces uniformity of
representations within each sample. We empirically study this approach on chest
X-ray tasks and find it to be very effective, outperforming methods without
local losses on 12 of 18 tasks.
