Sliding Window and Pseudo-labeling techniques for Cellular Segmentation

Minh Nguyen Hai, Duong Le Huy, Nam Nguyen The, Truong Bui Nhat, Tuyen Dam Trong, Hanh Le Thi, Anh Nguyen Kha Ngoc, Kien Le Trung, Anh Nguyen Cong Hoang, Anh Nguyen Ngoc, Duong Nguyen Hai
Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, PMLR 212:1-10, 2023.

Abstract

Cell segmentation is a fundamental task in biomedical image analysis, which involves the identification and separation of individual cells from microscopy images. Large-size images and unannotated data are two canailing problems degrading the performance in cell segmentation. Regarding these issues, we propose sliding window and pseudo-labeling techniques by conducting several experiments on different neural architectures. Following this approach, our method achieves a significant performance improvement and a final result of 0.8097 F1 score on the tuning set and 0.6379 F1 score on the test set of Weakly Supervised Cell Segmentation in Multi-modality Microscopy challenge hosted at NeurIPS 2022.

Cite this Paper

BibTeX
@InProceedings{pmlr-v212-nguyen-hai23a, title = {Sliding Window and Pseudo-labeling techniques for Cellular Segmentation}, author = {Nguyen Hai, Minh and Le Huy, Duong and Nguyen The, Nam and Bui Nhat, Truong and Dam Trong, Tuyen and Le Thi, Hanh and Nguyen Kha Ngoc, Anh and Le Trung, Kien and Nguyen Cong Hoang, Anh and Nguyen Ngoc, Anh and Nguyen Hai, Duong}, booktitle = {Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images}, pages = {1--10}, year = {2023}, editor = {Ma, Jun and Xie, Ronald and Gupta, Anubha and Guilherme de Almeida, José and Bader, Gary D. and Wang, Bo}, volume = {212}, series = {Proceedings of Machine Learning Research}, month = {28 Nov--09 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v212/nguyen-hai23a/nguyen-hai23a.pdf}, url = {https://proceedings.mlr.press/v212/nguyen-hai23a.html}, abstract = {Cell segmentation is a fundamental task in biomedical image analysis, which involves the identification and separation of individual cells from microscopy images. Large-size images and unannotated data are two canailing problems degrading the performance in cell segmentation. Regarding these issues, we propose sliding window and pseudo-labeling techniques by conducting several experiments on different neural architectures. Following this approach, our method achieves a significant performance improvement and a final result of 0.8097 F1 score on the tuning set and 0.6379 F1 score on the test set of Weakly Supervised Cell Segmentation in Multi-modality Microscopy challenge hosted at NeurIPS 2022.} }
Endnote
%0 Conference Paper %T Sliding Window and Pseudo-labeling techniques for Cellular Segmentation %A Minh Nguyen Hai %A Duong Le Huy %A Nam Nguyen The %A Truong Bui Nhat %A Tuyen Dam Trong %A Hanh Le Thi %A Anh Nguyen Kha Ngoc %A Kien Le Trung %A Anh Nguyen Cong Hoang %A Anh Nguyen Ngoc %A Duong Nguyen Hai %B Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images %C Proceedings of Machine Learning Research %D 2023 %E Jun Ma %E Ronald Xie %E Anubha Gupta %E José Guilherme de Almeida %E Gary D. Bader %E Bo Wang %F pmlr-v212-nguyen-hai23a %I PMLR %P 1--10 %U https://proceedings.mlr.press/v212/nguyen-hai23a.html %V 212 %X Cell segmentation is a fundamental task in biomedical image analysis, which involves the identification and separation of individual cells from microscopy images. Large-size images and unannotated data are two canailing problems degrading the performance in cell segmentation. Regarding these issues, we propose sliding window and pseudo-labeling techniques by conducting several experiments on different neural architectures. Following this approach, our method achieves a significant performance improvement and a final result of 0.8097 F1 score on the tuning set and 0.6379 F1 score on the test set of Weakly Supervised Cell Segmentation in Multi-modality Microscopy challenge hosted at NeurIPS 2022.
APA
Nguyen Hai, M., Le Huy, D., Nguyen The, N., Bui Nhat, T., Dam Trong, T., Le Thi, H., Nguyen Kha Ngoc, A., Le Trung, K., Nguyen Cong Hoang, A., Nguyen Ngoc, A. & Nguyen Hai, D.. (2023). Sliding Window and Pseudo-labeling techniques for Cellular Segmentation. Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, in Proceedings of Machine Learning Research 212:1-10 Available from https://proceedings.mlr.press/v212/nguyen-hai23a.html.

Related Material