# Parallel Capsule Networks for Classification of White Blood Cells

arXiv: https://arxiv.org/abs/2108.02644 · HTML: https://mlsanity.com/paper/2108.02644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Capsules for Object Segmentation](https://mlsanity.com/paper/1804.04241/) (2018)
- [Brain Tumor Type Classification via Capsule Networks](https://mlsanity.com/paper/1802.10200/) (2018)
- [Capsule Networks against Medical Imaging Data Challenges](https://mlsanity.com/paper/1807.07559/) (2018)
- [Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization](https://mlsanity.com/paper/1808.05744/) (2018)
- [Convolutional capsule network for classification of breast cancer histology images](https://mlsanity.com/paper/1804.08376/) (2018)
- [Self-Attention Capsule Networks for Object Classification](https://mlsanity.com/paper/1904.12483/) (2019)
- [Diagnosing Colorectal Polyps in the Wild with Capsule Networks](https://mlsanity.com/paper/2001.03305/) (2020)

## Abstract (arXiv)

Capsule Networks (CapsNets) is a machine learning architecture proposed to overcome some of the shortcomings of convolutional neural networks (CNNs). However, CapsNets have mainly outperformed CNNs in datasets where images are small and/or the objects to identify have minimal background noise. In this work, we present a new architecture, parallel CapsNets, which exploits the concept of branching the network to isolate certain capsules, allowing each branch to identify different entities. We applied our concept to the two current types of CapsNet architectures, studying the performance for networks with different layers of capsules. We tested our design in a public, highly unbalanced dataset of acute myeloid leukaemia images (15 classes). Our experiments showed that conventional CapsNets show similar performance than our baseline CNN (ResNeXt-50) but depict instability problems. In contrast, parallel CapsNets can outperform ResNeXt-50, is more stable, and shows better rotational invariance than both, conventional CapsNets and ResNeXt-50.