Capsule Networks against Medical Imaging Data Challenges

by   Amelia Jiménez-Sánchez, et al.

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.


Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification

Medical image analysis using supervised deep learning methods remains pr...

Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing

We explore three representative lines of research and demonstrate the ut...

Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network

Automating classification and segmentation process of abnormal regions i...

Spectral decoupling allows training transferable neural networks in medical imaging

Deep neural networks show impressive performance in medical imaging task...

Advanced Capsule Networks via Context Awareness

Capsule Networks (CN) offer new architectures for Deep Learning (DL) com...

MIXCAPS: A Capsule Network-based Mixture of Experts for Lung Nodule Malignancy Prediction

Lung diseases including infections such as Pneumonia, Tuberculosis, and ...

Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup

Image classification with deep neural networks has seen a surge of techn...