Data Augmentation using Feature Generation for Volumetric Medical Images

by   Khushboo Mehra, et al.

Medical image classification is one of the most critical problems in the image recognition area. One of the major challenges in this field is the scarcity of labelled training data. Additionally, there is often class imbalance in datasets as some cases are very rare to happen. As a result, accuracy in classification task is normally low. Deep Learning models, in particular, show promising results on image segmentation and classification problems, but they require very large datasets for training. Therefore, there is a need to generate more of synthetic samples from the same distribution. Previous work has shown that feature generation is more efficient and leads to better performance than corresponding image generation. We apply this idea in the Medical Imaging domain. We use transfer learning to train a segmentation model for the small dataset for which gold-standard class annotations are available. We extracted the learnt features and use them to generate synthetic features conditioned on class labels, using Auxiliary Classifier GAN (ACGAN). We test the quality of the generated features in a downstream classification task for brain tumors according to their severity level. Experimental results show a promising result regarding the validity of these generated features and their overall contribution to balancing the data and improving the classification class-wise accuracy.


page 3

page 4

page 6

page 7


Capsule Networks against Medical Imaging Data Challenges

A key component to the success of deep learning is the availability of m...

Synthetic Sample Selection via Reinforcement Learning

Synthesizing realistic medical images provides a feasible solution to th...

GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification

A major challenge in applying deep learning to medical imaging is the pa...

Adversarial Policy Gradient for Deep Learning Image Augmentation

The use of semantic segmentation for masking and cropping input images h...

Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy

We introduce the idea of inter-slice image augmentation whereby the numb...

Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective

Exploiting learning algorithms under scarce data regimes is a limitation...

Improving CNN Training using Disentanglement for Liver Lesion Classification in CT

Training data is the key component in designing algorithms for medical i...

Please sign up or login with your details

Forgot password? Click here to reset