Keywords: Cell Segmentation
TL;DR: A solution to NeurIPS 2022 Weakly Supervised Cell Segmentation
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.