# C2G-Net: Exploiting Morphological Properties for Image Classification

Source: [C2G-Net: Exploiting Morphological Properties for Image Classification](<https://www.sota2.com/research/paper/68fa67ec1e209131cea192ca>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:03:45.482Z

Authors: Laurin Herbsthofer, Barbara Prietl, Martina Tomberger, Thomas Pieber, Pablo L\\'opez-Garc\\'ia

Published: 2020-07-07T12:16:17.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.03378>)

[PDF](<https://arxiv.org/pdf/2007.03378.pdf>)