2020/07/07 by Laurin Herbsthofer, Barbara Prietl, Herbsthofer, Laurin +8
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Medicine · #AI in cancer detection #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Radiomics and Machine Learning in Medical Imaging #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2007.03378
10 pages, 5 figures (Figure 3 with 4 sub-figures), Appendix A and Appendix B after the references. Originally submitted to ICML2020 but rejected
arxiv created 2020/07/07 · openalex publication_date 2020/07/07 · arxiv updated 2020/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we propose C2G-Net, a pipeline for image classification that exploits the morphological properties of images containing a large number of similar objects like biological cells. C2G-Net consists of two components: (1) Cell2Grid, an image compression algorithm that identifies objects using segmentation and arranges them on a grid, and (2) DeepLNiNo, a CNN architecture with less than 10,000 trainable parameters aimed at facilitating model interpretability. To test the performance of C2G-Net we used multiplex immunohistochemistry images for predicting relapse risk in colon cancer. Compared to conventional CNN architectures trained on raw images, C2G-Net achieved similar prediction accuracy while training time was reduced by 85% and its model was is easier to interpret.