An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation

02/01/2022
by   Hritam Basak, et al.
0

The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. In this paper, we introduce a novel regularization strategy involving interpolation-based mixing for semi-supervised medical image segmentation. The proposed method is a new consistency regularization strategy that encourages segmentation of interpolation of two unlabelled data to be consistent with the interpolation of segmentation maps of those data. This method represents a specific type of data-adaptive regularization paradigm which aids to minimize the overfitting of labelled data under high confidence values. The proposed method is advantageous over adversarial and generative models as it requires no additional computation. Upon evaluation on two publicly available MRI datasets: ACDC and MMWHS, experimental results demonstrate the superiority of the proposed method in comparison to existing semi-supervised models. Code is available at: https://github.com/hritam-98/ICT-MedSeg

READ FULL TEXT
research
08/25/2021

Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation

Segmentation of images is a long-standing challenge in medical AI. This ...
research
07/06/2023

Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning

Although unsupervised domain adaptation (UDA) is a promising direction t...
research
03/28/2022

Translation Consistent Semi-supervised Segmentation for 3D Medical Images

3D medical image segmentation methods have been successful, but their de...
research
08/29/2019

Temporal Consistency Objectives Regularize the Learning of Disentangled Representations

There has been an increasing focus in learning interpretable feature rep...
research
03/02/2022

Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

Semi-supervised segmentation remains challenging in medical imaging sinc...
research
08/12/2022

Triple-View Feature Learning for Medical Image Segmentation

Deep learning models, e.g. supervised Encoder-Decoder style networks, ex...
research
09/20/2021

Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation

Consistency training has proven to be an advanced semi-supervised framew...

Please sign up or login with your details

Forgot password? Click here to reset