2021/06/29 by Neda Zamanitajeddin, Zamanitajeddin, Neda, Mostafa Jahanifar +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #AI in cancer detection #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Radiomics and Machine Learning in Medical Imaging #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2106.15299
openalex publication_date 2021/06/29 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Digitization of histology images and the advent of new computational methods,\nlike deep learning, have helped the automatic grading of colorectal\nadenocarcinoma cancer (CRA). Present automated CRA grading methods, however,\nusually use tiny image patches and thus fail to integrate the entire tissue\nmicro-architecture for grading purposes. To tackle these challenges, we propose\nto use a statistical network analysis method to describe the complex structure\nof the tissue micro-environment by modelling nuclei and their connections as a\nnetwork. We show that by analyzing only the interactions between the cells in a\nnetwork, we can extract highly discriminative statistical features for CRA\ngrading. Unlike other deep learning or convolutional graph-based approaches,\nour method is highly scalable (can be used for cell networks consist of\nmillions of nodes), completely explainable, and computationally inexpensive. We\ncreate cell networks on a broad CRC histology image dataset, experiment with\nour method, and report state-of-the-art performance for the prediction of\nthree-class CRA grading.\n