8 citations · 26 across the 11 of their papers we have counts for
11 papers
Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue
Kengo Araki, Mariyo Rokutan-Kurata, Kazuhiro Terada +2
Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used si…
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…
Negative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathology
Hiroki Tokunaga, Brian Kenji Iwana, Yuki Teramoto +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…