DeepAI AI Chat
Log In Sign Up

Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images

by   Bruno Lecouat, et al.
Harvard University
Agency for Science, Technology and Research

Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require resource-intensive expert annotation. Semi-supervised generative adversarial network (GAN) approaches offer a means to learn from limited labeled data alongside larger unlabeled datasets, but have not been applied to discern fine-scale, sparse or localized features that define medical abnormalities. To overcome these limitations, we propose a patch-based semi-supervised learning approach and evaluate performance on classification of diabetic retinopathy from funduscopic images. Our semi-supervised approach achieves high AUC with just 10-20 labeled training images, and outperforms the supervised baselines by upto 15 of the training dataset is labeled. Further, our method implicitly enables interpretation of the SSL predictions. As this approach enables good accuracy, resolution and interpretability with lower annotation burden, it sets the pathway for scalable applications of deep learning in clinical imaging.


page 1

page 2

page 3

page 4

page 5

page 8


3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework

The success of deep learning for medical imaging tasks, such as classifi...

A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms

Semi-supervised image classification has shown substantial progress in l...

Read classification using semi-supervised deep learning

In this paper, we propose a semi-supervised deep learning method for det...

Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach

Contemporary deep learning based medical image segmentation algorithms r...

Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet

Catheter segmentation in 3D ultrasound is important for computer-assiste...

Semi-supervised Semantic Segmentation of Organs at Risk on 3D Pelvic CT Images

Automated segmentation of organs-at-risk in pelvic computed tomography (...

Code Repositories


Implemenation of Semi-Supervised Learning using GANs in PyTorch for MNIST and CIFAR-10 datasets

view repo