# Capsule Networks against Medical Imaging Data Challenges

arXiv: https://arxiv.org/abs/1807.07559 · HTML: https://mlsanity.com/paper/1807.07559/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Diversity Enhanced Active Learning with Strictly Proper Scoring Rules](https://mlsanity.com/paper/2110.14171/) (2021)
- [Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning](https://mlsanity.com/paper/2312.10116/) (2023)
- [Mask of truth: model sensitivity to unexpected regions of medical images](https://mlsanity.com/paper/2412.04030/) (2024)
- [Deformable Capsules for Object Detection](https://mlsanity.com/paper/2104.05031/) (2021)
- [3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image Segmentation](https://mlsanity.com/paper/2205.09299/) (2022)
- [COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing](https://mlsanity.com/paper/2102.06169/) (2021)
- [Parallel Capsule Networks for Classification of White Blood Cells](https://mlsanity.com/paper/2108.02644/) (2021)
- [PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification](https://mlsanity.com/paper/2306.16918/) (2023)
- [P2W: From Power Traces to Weights Matrix -- An Unconventional Transfer Learning Approach](https://mlsanity.com/paper/2502.14968/) (2025)

## Abstract (arXiv)

A key component to the success of deep learning is the availability of massive amounts of training data. Building and annotating large datasets for solving medical image classification problems is today a bottleneck for many applications. Recently, capsule networks were proposed to deal with shortcomings of Convolutional Neural Networks (ConvNets). In this work, we compare the behavior of capsule networks against ConvNets under typical datasets constraints of medical image analysis, namely, small amounts of annotated data and class-imbalance. We evaluate our experiments on MNIST, Fashion-MNIST and medical (histological and retina images) publicly available datasets. Our results suggest that capsule networks can be trained with less amount of data for the same or better performance and are more robust to an imbalanced class distribution, which makes our approach very promising for the medical imaging community.