  Dense prediction tasks such as segmentation and detection of pathological
entities hold crucial clinical value in computational pathology workflows.
However, obtaining dense annotations on large cohorts is usually tedious and
expensive. Contrastive learning (CL) is thus often employed to leverage large
volumes of unlabeled data to pre-train the backbone network. To boost CL for
dense prediction, some studies have proposed variations of dense matching
objectives in pre-training. However, our analysis shows that employing existing
dense matching strategies on histopathology images enforces invariance among
incorrect pairs of dense features and, thus, is imprecise. To address this, we
propose a precise location-based matching mechanism that utilizes the
overlapping information between geometric transformations to precisely match
regions in two augmentations. Extensive experiments on two pretraining datasets
(TCGA-BRCA, NCT-CRC-HE) and three downstream datasets (GlaS, CRAG, BCSS)
highlight the superiority of our method in semantic and instance segmentation
tasks. Our method outperforms previous dense matching methods by up to 7.2% in
average precision for detection and 5.6% in average precision for instance
segmentation tasks. Additionally, by using our matching mechanism in the three
popular contrastive learning frameworks, MoCo-v2, VICRegL, and ConCL, the
average precision in detection is improved by 0.7% to 5.2%, and the average
precision in segmentation is improved by 0.7% to 4.0%, demonstrating
generalizability. Our code is available at
https://github.com/cvlab-stonybrook/PLM_SSL.
