C2G-Net: Exploiting Morphological Properties for Image Classification

Autor:	Herbsthofer, Laurin, Prietl, Barbara, Tomberger, Martina, Pieber, Thomas, López-García, Pablo
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages, 5 figures (Figure 3 with 4 sub-figures), Appendix A and Appendix B after the references. Originally submitted to ICML2020 but rejected
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.03378 Zobrazit plný text záznamu View this record from Arxiv