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The presence of tertiary lymphoid structures (TLSs) on pancreaticpathological images is an important prognostic indicator of pancreatic tumors.Therefore, TLSs detection on pancreatic pathological images plays a crucialrole in diagnosis and treatment for patients with pancreatic tumors. However,fully supervised detection algorithms based on deep learning usually require alarge number of manual annotations, which is time-consuming andlabor-intensive. In this paper, we aim to detect the TLSs in a manner offew-shot learning by proposing a weakly supervised segmentation network. Wefirstly obtain the lymphocyte density maps by combining a pretrained model fornuclei segmentation and a domain adversarial network for lymphocyte nucleirecognition. Then, we establish a cross-scale attention guidance mechanism byjointly learning the coarse-scale features from the original histopathologyimages and fine-scale features from our designed lymphocyte density attention.A noise-sensitive constraint is introduced by an embedding signed distancefunction loss in the training procedure to reduce tiny prediction errors.Experimental results on two collected datasets demonstrate that our proposedmethod significantly outperforms the state-of-the-art segmentation-basedalgorithms in terms of TLSs detection accuracy. Additionally, we apply ourmethod to study the congruent relationship between the density of TLSs andperipancreatic vascular invasion and obtain some clinically statisticalresults.

A Weakly Supervised Segmentation Network Embedding Cross-scale Attention  Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid  Structures of Pancreatic Tumors