  The presence of tertiary lymphoid structures (TLSs) on pancreatic
pathological images is an important prognostic indicator of pancreatic tumors.
Therefore, TLSs detection on pancreatic pathological images plays a crucial
role in diagnosis and treatment for patients with pancreatic tumors. However,
fully supervised detection algorithms based on deep learning usually require a
large number of manual annotations, which is time-consuming and
labor-intensive. In this paper, we aim to detect the TLSs in a manner of
few-shot learning by proposing a weakly supervised segmentation network. We
firstly obtain the lymphocyte density maps by combining a pretrained model for
nuclei segmentation and a domain adversarial network for lymphocyte nuclei
recognition. Then, we establish a cross-scale attention guidance mechanism by
jointly learning the coarse-scale features from the original histopathology
images and fine-scale features from our designed lymphocyte density attention.
A noise-sensitive constraint is introduced by an embedding signed distance
function loss in the training procedure to reduce tiny prediction errors.
Experimental results on two collected datasets demonstrate that our proposed
method significantly outperforms the state-of-the-art segmentation-based
algorithms in terms of TLSs detection accuracy. Additionally, we apply our
method to study the congruent relationship between the density of TLSs and
peripancreatic vascular invasion and obtain some clinically statistical
results.
