It is challenging to obtain a sufficient amount of high-quality annotated images for deep-learning applications in medical imaging, and practical methods often use a combination of labelled and unlabelled data. A dual-view framework builds on such semi-supervised approaches and uses two independently trained critic networks that learn from each other to generate segmentation masks in different medical imaging modalities.
- Himashi Peiris
- Munawar Hayat
- Mehrtash Harandi
