Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping

Sajid Javed, Arif Mahmood, Naoufel Werghi, Ksenija Benes, Nasir Rajpoot

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


In computational pathology, automated tissue phenotyping in cancer histology images is a fundamental tool for profiling tumor microenvironments. Current tissue phenotyping methods use features derived from image patches which may not carry biological significance. In this work, we propose a novel multiplex cellular community-based algorithm for tissue phenotyping integrating cell-level features within a graph-based hierarchical framework. We demonstrate that such integration offers better performance compared to prior deep learning and texture-based methods as well as to cellular community based methods using uniplex networks. To this end, we construct cell-level graphs using texture, alpha diversity and multi-resolution deep features. Using these graphs, we compute cellular connectivity features which are then employed for the construction of a patch-level multiplex network. Over this network, we compute multiplex cellular communities using a novel objective function. The proposed objective function computes a low-dimensional subspace from each cellular network and subsequently seeks a common low-dimensional subspace using the Grassmann manifold. We evaluate our proposed algorithm on three publicly available datasets for tissue phenotyping, demonstrating a significant improvement over existing state-of-the-art methods.

Original languageBritish English
Article number9204851
Pages (from-to)9204-9219
Number of pages16
JournalIEEE Transactions on Image Processing
StatePublished - 2020


  • Cellular community detection
  • colon cancer
  • computational pathology
  • tissue phenotyping


Dive into the research topics of 'Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping'. Together they form a unique fingerprint.

Cite this