PaCaS-WAA: Patch-Based Contrastive Semi-Supervised Learning with Wavelet Guidance and Adaptive Augmentation for Tumour Segmentation
Xiong, Wanqing; Chen, Zailiang; Liu, Qing; Wu, Wenjia; Zhang, Jian; Shen, Hailan (2024-03-18)
IEEE
18.03.2024
W. Xiong, Z. Chen, Q. Liu, W. Wu, J. Zhang and H. Shen, "PaCaS-WAA: Patch-Based Contrastive Semi-Supervised Learning with Wavelet Guidance and Adaptive Augmentation for Tumour Segmentation," ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp. 12941-12945, doi: 10.1109/ICASSP48485.2024.10446202
https://rightsstatements.org/vocab/InC/1.0/
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405284011
https://urn.fi/URN:NBN:fi:oulu-202405284011
Tiivistelmä
Abstract
In many image-guided clinical approaches, tumor segmentation is a fundamental and critical step for locating tumor involvement. However, the scarcity of annotated data and the low contrast of medical imaging techniques make it challenging to accurately segment tumors from surrounding tissues using supervised learning methods. To address these issues, we propose a patch-based contrastive semi-supervised learning framework with wavelet guidance and adaptive data augmentation (PaCaS-WAA). Specifically, we apply patch-based contrast to maintain high-quality segmentation results with limited labels. Moreover, to exploit the discriminative information about subtle boundaries, we use the wavelet domain guides UNet for more edge details. Besides, to increase the diversity of unlabelled data, we propose an adaptive data augmentation strategy to augment the unlabelled data according to its Challenging Grade. Experimental results on two publicly available datasets of different modalities demonstrate that our method consistently outperform the state-of-the-art semi-supervised segmentation methods.
In many image-guided clinical approaches, tumor segmentation is a fundamental and critical step for locating tumor involvement. However, the scarcity of annotated data and the low contrast of medical imaging techniques make it challenging to accurately segment tumors from surrounding tissues using supervised learning methods. To address these issues, we propose a patch-based contrastive semi-supervised learning framework with wavelet guidance and adaptive data augmentation (PaCaS-WAA). Specifically, we apply patch-based contrast to maintain high-quality segmentation results with limited labels. Moreover, to exploit the discriminative information about subtle boundaries, we use the wavelet domain guides UNet for more edge details. Besides, to increase the diversity of unlabelled data, we propose an adaptive data augmentation strategy to augment the unlabelled data according to its Challenging Grade. Experimental results on two publicly available datasets of different modalities demonstrate that our method consistently outperform the state-of-the-art semi-supervised segmentation methods.
Kokoelmat
- Avoin saatavuus [37647]