CGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images

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dc.citation.articleNumber727610
dc.citation.journalTitleFrontiers in Immunology
dc.citation.volumeNumber12
dc.contributor.authorBaranwal, Mayank
dc.contributor.authorKrishnan, Santhoshi
dc.contributor.authorOneka, Morgan
dc.contributor.authorFrankel, Timothy
dc.contributor.authorRao, Arvind
dc.date.accessioned2021-10-28T18:47:22Z
dc.date.available2021-10-28T18:47:22Z
dc.date.issued2021
dc.description.abstractEarly detection of Pancreatic Ductal Adenocarcinoma (PDAC), one of the most aggressive malignancies of the pancreas, is crucial to avoid metastatic spread to other body regions. Detection of pancreatic cancer is typically carried out by assessing the distribution and arrangement of tumor and immune cells in histology images. This is further complicated due to morphological similarities with chronic pancreatitis (CP), and the co-occurrence of precursor lesions in the same tissue. Most of the current automated methods for grading pancreatic cancers rely on extensive feature engineering involving accurate identification of cell features or utilising single number spatially informed indices for grading purposes. Moreover, sophisticated methods involving black-box approaches, such as neural networks, do not offer insights into the model’s ability to accurately identify the correct disease grade. In this paper, we develop a novel cell-graph based Cell-Graph Attention (CGAT) network for the precise classification of pancreatic cancer and its precursors from multiplexed immunofluorescence histology images into the six different types of pancreatic diseases. The issue of class imbalance is addressed through bootstrapping multiple CGAT-nets, while the self-attention mechanism facilitates visualization of cell-cell features that are likely responsible for the predictive capabilities of the model. It is also shown that the model significantly outperforms the decision tree classifiers built using spatially informed metric, such as the Morisita-Horn (MH) indices.
dc.identifier.citationBaranwal, Mayank, Krishnan, Santhoshi, Oneka, Morgan, et al.. "CGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images." <i>Frontiers in Immunology,</i> 12, (2021) Frontiers Media S.A.: https://doi.org/10.3389/fimmu.2021.727610.
dc.identifier.digitalfimmu-12-727610
dc.identifier.doihttps://doi.org/10.3389/fimmu.2021.727610
dc.identifier.urihttps://hdl.handle.net/1911/111617
dc.language.isoeng
dc.publisherFrontiers Media S.A.
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleCGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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