8 citations · 17 across the 8 of their papers we have counts for
7 papers
Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification
Kazuma Fujii, Daiki Suehiro, Kazuya Nishimura +1
Cell detection is an essential task in cell image analysis. Recent deep learning-based detection methods have achieved very promising results. In general, these methods require exh…
Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap
Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe +1
The domain shift problem is an important issue in automatic cell detection. A detection network trained with training data under a specific condition (source domain) may not work w…
Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency
Kazuya Nishimura, Hyeonwoo Cho, Ryoma Bise
Cell detection is the task of detecting the approximate positions of cell centroids from microscopy images. Recently, convolutional neural network-based approaches have achieved pr…
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation
Kazuya Nishimura, Junya Hayashida, Chenyang Wang +2
We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i.e., the coordinates of c…
Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN
Kazuya Nishimura, Ryoma Bise
Automated mitotic detection in time-lapse phasecontrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been pro…
MPM: Joint Representation of Motion and Position Map for Cell Tracking
Junya Hayashida, Kazuya Nishimura, Ryoma Bise
Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association). Most cell track…