TY - GEN
T1 - SISU
T2 - 2nd International Workshop on Trustworthy Artificial Intelligence for Healthcare, TAI4H 2024
AU - Kha, Hien Quang
AU - Le, Minh Huu Nhat
AU - Nguyen, Lam Huu Phuc
AU - Tran, Minh Nguyen Tuan
AU - Nguyen, Linh My
AU - Thong, Hung Quay
AU - Le, Nguyen Quoc Khanh
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The automated segmentation of leukocytes plays a vital role in diagnosing and monitoring life-threatening conditions such as leukemia and lymphoma. However, this task encounters challenges due to the limited availability and quality of public datasets. To effectively utilize the limited dataset, it is necessary to develop a semi-supervised learning framework. In this regard, we propose a holistic self-training framework called Self-training using In-turn Supervised-Unsupervised training (SISU). Our framework encompasses two key components. Firstly, we introduce Feature Perturbed Cross-View Co-Training, which incorporates dual feature perturbation methods and utilizes two auxiliary decoders to enhance the robustness of the feature representation. Secondly, drawing inspiration from FixMatch, we integrate a regularization mechanism via weak-to-strong consistency training to further enhance the self-training framework. Finally, we conduct training and evaluation of SISU for semi-supervised learning on three datasets (Zheng 1, Zheng 2, and LISC), achieving remarkable mIOU scores of up to 93.54%, 88.92%, and 77.02% respectively across multiple settings within the self-training scheme.
AB - The automated segmentation of leukocytes plays a vital role in diagnosing and monitoring life-threatening conditions such as leukemia and lymphoma. However, this task encounters challenges due to the limited availability and quality of public datasets. To effectively utilize the limited dataset, it is necessary to develop a semi-supervised learning framework. In this regard, we propose a holistic self-training framework called Self-training using In-turn Supervised-Unsupervised training (SISU). Our framework encompasses two key components. Firstly, we introduce Feature Perturbed Cross-View Co-Training, which incorporates dual feature perturbation methods and utilizes two auxiliary decoders to enhance the robustness of the feature representation. Secondly, drawing inspiration from FixMatch, we integrate a regularization mechanism via weak-to-strong consistency training to further enhance the self-training framework. Finally, we conduct training and evaluation of SISU for semi-supervised learning on three datasets (Zheng 1, Zheng 2, and LISC), achieving remarkable mIOU scores of up to 93.54%, 88.92%, and 77.02% respectively across multiple settings within the self-training scheme.
KW - Consistency Regularization
KW - FixMatch
KW - Leukemia
KW - Lymphoma
KW - Semi-supervised learning
KW - White blood cell
UR - http://www.scopus.com/inward/record.url?scp=85201237264&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85201237264&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-67751-9_11
DO - 10.1007/978-3-031-67751-9_11
M3 - Conference contribution
AN - SCOPUS:85201237264
SN - 9783031677502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 134
EP - 144
BT - Trustworthy Artificial Intelligence for Healthcare - 2nd International Workshop, TAI4H 2024, Proceedings
A2 - Chen, Hao
A2 - Zhou, Yuyin
A2 - Xu, Daguang
A2 - Vardhanabhuti, Varut Vince
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 August 2024 through 4 August 2024
ER -