2021/06/30 by Pingjun Chen, Muhammad Aminu, Chen, Pingjun +7
Biochemistry, Genetics and Molecular Biology · Computer Science · #68T01 (Primary) #AI in cancer detection #Cancer Genomics and Diagnostics #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.10 #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.16174
openalex publication_date 2021/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The cells and their spatial patterns in the tumor microenvironment (TME) play a key role in tumor evolution, and yet the latter remains an understudied topic in computational pathology. This study, to the best of our knowledge, is among the first to hybridize local and global graph methods to profile orchestration and interaction of cellular components. To address the challenge in hematolymphoid cancers, where the cell classes in TME may be unclear, we first implemented cell-level unsupervised learning and identified two new cell subtypes. Local cell graphs or supercells were built for each image by considering the individual cell's geospatial location and classes. Then, we applied supercell level clustering and identified two new cell communities. In the end, we built global graphs to abstract spatial interaction patterns and extract features for disease diagnosis. We evaluate the proposed algorithm on H&E slides of 60 hematolymphoid neoplasms and further compared it with three cell level graph-based algorithms, including the global cell graph, cluster cell graph, and FLocK. The proposed algorithm achieved a mean diagnosis accuracy of 0.703 with the repeated 5-fold cross-validation scheme. In conclusion, our algorithm shows superior performance over the existing methods and can be potentially applied to other cancer types.