Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model

by   Quande Liu, et al.

Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accurately annotating medical images demands expertise knowledge of the clinicians. In this paper, we present a novel relation-driven semi-supervised framework for medical image classification. It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data. Considering that human diagnosis often refers to previous analogous cases to make reliable decisions, we introduce a novel sample relation consistency (SRC) paradigm to effectively exploit unlabeled data by modeling the relationship information among different samples. Superior to existing consistency-based methods which simply enforce consistency of individual predictions, our framework explicitly enforces the consistency of semantic relation among different samples under perturbations, encouraging the model to explore extra semantic information from unlabeled data. We have conducted extensive experiments to evaluate our method on two public benchmark medical image classification datasets, i.e.,skin lesion diagnosis with ISIC 2018 challenge and thorax disease classification with ChestX-ray14. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.


page 1

page 4

page 5

page 9

page 11


Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

Training deep convolutional neural networks usually requires a large amo...

Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification

Intelligent medical diagnosis has shown remarkable progress based on the...

Semi-supervised Medical Image Classification with Global Latent Mixing

Computer-aided diagnosis via deep learning relies on large-scale annotat...

Towards Open-Scenario Semi-supervised Medical Image Classification

Semi-supervised learning (SSL) has attracted much attention since it red...

ACPL: Anti-curriculum Pseudo-labelling forSemi-supervised Medical Image Classification

Effective semi-supervised learning (SSL) in medical im-age analysis (MIA...

Robust Learning at Noisy Labeled Medical Images:Applied to Skin Lesion Classification

Deep neural networks (DNNs) have achieved great success in a wide variet...

Weakly-supervised Generative Adversarial Networks for medical image classification

Weakly-supervised learning has become a popular technology in recent yea...

Please sign up or login with your details

Forgot password? Click here to reset