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Found 9 papers, 9 papers with code
Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping
First, we generate an image pair not containing mitosis events by frame-order flipping.
Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection
In particular, cells are cultured under different conditions depending on the purpose of the research.
Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification
Our proposed method takes a pseudo labeling approach for cell detection from imperfect annotated data.
Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency
This annotation is a time-consuming and labor-intensive task.
Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap
We propose an unsupervised domain adaptation method for cell detection using the pseudo-cell-position heatmap, where a cell centroid becomes a peak with a Gaussian distribution in the map.
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation
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 cell positions) without association information, in which cell positions can be easily obtained by nuclear staining.
Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN
In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating a spatiotemporal likelihood map by 3DCNN.
MPM: Joint Representation of Motion and Position Map for Cell Tracking
Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association).
Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response
In addition, we demonstrated that our method can perform without any annotation by using fluorescence images that cell nuclear were stained as training data.
Cell Segmentation
Cultural Vocal Bursts Intensity Prediction
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