Semi-supervised Unpaired Medical Image Segmentation Through Task-affinity Consistency

Jingkun Chen, Jianguo Zhang, Kurt Debattista, Jungong Han

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

115 Wedi eu Llwytho i Lawr (Pure)


Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of manual annotation of clinicians by using unlabelled data, when developing medical image segmentation tools. However, to date, most existing semi-supervised learning (SSL) algorithms treat the labelled images and unlabelled images separately and ignore the explicit connection between them; this disregards essential shared information and thus hinders further performance improvements. To mine the shared information between the labelled and unlabelled images, we introduce a class-specific representation extraction approach, in which a task-affinity module is specifically designed for representation extraction. We further cast the representation into two different views of feature maps; one is focusing on low-level context, while the other concentrates on structural information. The two views of feature maps are incorporated into the task-affinity module, which then extracts the class-specific representations to aid the knowledge transfer from the labelled images to the unlabelled images. In particular, a task-affinity consistency loss between the labelled images and unlabelled images based on the multi-scale class-specific representations is formulated, leading to a significant performance improvement. Experimental results on three datasets show that our method consistently outperforms existing state-of-the-art methods. Our findings highlight the potential of consistency between class-specific knowledge for semi-supervised medical image segmentation. The code and models are to be made publicly available at
Iaith wreiddiolSaesneg
Tudalennau (o-i)594-605
Nifer y tudalennau12
CyfnodolynIEEE Transactions on Medical Imaging
Rhif cyhoeddi3
Dyddiad ar-lein cynnar11 Hyd 2022
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Maw 2023

Ôl bys

Gweld gwybodaeth am bynciau ymchwil 'Semi-supervised Unpaired Medical Image Segmentation Through Task-affinity Consistency'. Gyda’i gilydd, maen nhw’n ffurfio ôl bys unigryw.

Dyfynnu hyn