  Annotating medical imaging datasets is costly, so fine-tuning (or transfer
learning) is the most effective method for digital pathology vision
applications such as disease classification and semantic segmentation. However,
due to texture bias in models trained on real-world images, transfer learning
for histopathology applications might result in underperforming models, which
necessitates the need for using unlabeled histopathology data and
self-supervised methods to discover domain-specific characteristics. Here, we
tested the premise that histopathology-specific pretrained models provide
better initializations for pathology vision tasks, i.e., gland and cell
segmentation. In this study, we compare the performance of gland and cell
segmentation tasks with histopathology domain-specific and non-domain-specific
(real-world images) pretrained weights. Moreover, we investigate the dataset
size at which domain-specific pretraining produces significant gains in
performance. In addition, we investigated whether domain-specific
initialization improves the effectiveness of out-of-distribution testing on
distinct datasets but the same task. The results indicate that performance gain
using domain-specific pretrained weights depends on both the task and the size
of the training dataset. In instances with limited dataset sizes, a significant
improvement in gland segmentation performance was also observed, whereas models
trained on cell segmentation datasets exhibit no improvement.
