An Exceedingly Simple Consistency Regularization Method For Semi-Supervised Medical Image Segmentation
- Resource Type
- Conference
- Authors
- Basak, Hritam; Bhattacharya, Rajarshi; Hussain, Rukhshanda; Chatterjee, Agniv
- Source
- 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Biomedical Imaging (ISBI), 2022 IEEE 19th International Symposium on. :1-4 Mar, 2022
- Subject
- Bioengineering
Computing and Processing
Photonics and Electrooptics
Signal Processing and Analysis
Image segmentation
Interpolation
Codes
Annotations
Computational modeling
Magnetic resonance imaging
Biological system modeling
Semi-supervised Learning
Medical Image Segmentation
Consistency Regularization
- Language
- ISSN
- 1945-8452
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 un-der 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