1. CGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images.
- Author
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Baranwal M, Krishnan S, Oneka M, Frankel T, and Rao A
- Subjects
- Adult, Deep Learning, Female, Humans, Male, Middle Aged, Phenotype, Models, Theoretical, Pancreatic Diseases classification, Pancreatic Diseases pathology
- Abstract
Early 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., Competing Interests: AR has a consulting agreement with Voxel analytics LLC and consults for Genophyll, LLC. MB is currently employed with the Division of Data and Decision Sciences, Tata Consultancy Services, India. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funders were not involved in the study design, collection, analysis, and interpretation of data, the writing of this article or the decision to submit it for publication., (Copyright © 2021 Baranwal, Krishnan, Oneka, Frankel and Rao.)
- Published
- 2021
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