# Emergent symbolic language based deep medical image classification

arXiv: https://arxiv.org/abs/2008.09860 · HTML: https://mlsanity.com/paper/2008.09860/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024)
- [Neuro-Symbolic Learning: Principles and Applications in Ophthalmology](https://mlsanity.com/paper/2208.00374/) (2022)
- [ELCC: the Emergent Language Corpus Collection](https://mlsanity.com/paper/2407.04158/) (2024)
- [MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification](https://mlsanity.com/paper/2404.08917/) (2024)
- [Grounded Language Acquisition From Object and Action Imagery](https://mlsanity.com/paper/2309.06335/) (2023)
- [Emergent Language Symbolic Autoencoder (ELSA) with Weak Supervision to Model Hierarchical Brain Networks](https://mlsanity.com/paper/2404.10031/) (2024)

## Abstract (arXiv)

Modern deep learning systems for medical image classification have demonstrated exceptional capabilities for distinguishing between image based medical categories. However, they are severely hindered by their ina-bility to explain the reasoning behind their decision making. This is partly due to the uninterpretable continuous latent representations of neural net-works. Emergent languages (EL) have recently been shown to enhance the capabilities of neural networks by equipping them with symbolic represen-tations in the framework of referential games. Symbolic representations are one of the cornerstones of highly explainable good old fashioned AI (GOFAI) systems. In this work, we demonstrate for the first time, the emer-gence of deep symbolic representations of emergent language in the frame-work of image classification. We show that EL based classification models can perform as well as, if not better than state of the art deep learning mod-els. In addition, they provide a symbolic representation that opens up an entire field of possibilities of interpretable GOFAI methods involving symbol manipulation. We demonstrate the EL classification framework on immune cell marker based cell classification and chest X-ray classification using the CheXpert dataset. Code is available online at https://github.com/AriChow/EL.