Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification

03/05/2021
by   Fengbei Liu, et al.
9

The training of deep learning models generally requires a large amount of annotated data for effective convergence and generalisation. However, obtaining high-quality annotations is a laboursome and expensive process due to the need of expert radiologists for the labelling task. The study of semi-supervised learning in medical image analysis is then of crucial importance given that it is much less expensive to obtain unlabelled images than to acquire images labelled by expert radiologists.Essentially, semi-supervised methods leverage large sets of unlabelled data to enable better training convergence and generalisation than if we use only the small set of labelled images.In this paper, we propose the Self-supervised Mean Teacher for Semi-supervised (S^2MTS^2) learning that combines self-supervised mean-teacher pre-training with semi-supervised fine-tuning. The main innovation of S^2MTS^2 is the self-supervised mean-teacher pre-training based on the joint contrastive learning, which uses an infinite number of pairs of positive query and key features to improve the mean-teacher representation. The model is then fine-tuned using the exponential moving average teacher framework trained with semi-supervised learning.We validate S^2MTS^2 on the thorax disease multi-label classification problem from the dataset Chest X-ray14, where we show that it outperforms the previous SOTA semi-supervised learning methods by a large margin.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/09/2019

S^4L: Self-Supervised Semi-Supervised Learning

This work tackles the problem of semi-supervised learning of image class...
research
07/04/2022

S^5Mars: Self-Supervised and Semi-Supervised Learning for Mars Segmentation

Deep learning has become a powerful tool for Mars exploration. Mars terr...
research
09/05/2022

Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification

Deep learning approaches achieve state-of-the-art performance for classi...
research
08/10/2021

Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean

Deep learning models achieve strong performance for radiology image clas...
research
05/21/2019

Semi-Supervised Learning with Scarce Annotations

While semi-supervised learning (SSL) algorithms provide an efficient way...
research
01/21/2021

Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning

We present a plug-in replacement for batch normalization (BN) called exp...
research
06/19/2023

Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment

Hyperspectral image (HSI) classification is gaining a lot of momentum in...

Please sign up or login with your details

Forgot password? Click here to reset