1 code implementation • 22 Nov 2024 • Kaito Shiku, Kazuya Nishimura, Daiki Suehiro, Kiyohito Tanaka, Ryoma Bise
Patient-level diagnosis of severity in ulcerative colitis (UC) is common in real clinical settings, where the most severe score in a patient is recorded.
no code implementations • 8 May 2024 • Takumi Okuo, Kazuya Nishimura, Hiroaki Ito, Kazuhiro Terada, Akihiko Yoshizawa, Ryoma Bise
The PD-L1 rate, the number of PD-L1 positive tumor cells over the total number of all tumor cells, is an important metric for immunotherapy.
1 code implementation • 9 Jul 2023 • Kazuya Nishimura, Ami Katanaya, Shinichiro Chuma, Ryoma Bise
First, we generate an image pair not containing mitosis events by frame-order flipping.
1 code implementation • 9 Mar 2023 • Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe, Ryoma Bise
In particular, cells are cultured under different conditions depending on the purpose of the research.
1 code implementation • 20 Jul 2021 • Kazuma Fujii, Daiki Suehiro, Kazuya Nishimura, Ryoma Bise
Our proposed method takes a pseudo labeling approach for cell detection from imperfect annotated data.
2 code implementations • 19 Jul 2021 • Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe, Ryoma Bise
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.
1 code implementation • 19 Jul 2021 • Kazuya Nishimura, Hyeonwoo Cho, Ryoma Bise
This annotation is a time-consuming and labor-intensive task.
1 code implementation • ECCV 2020 • Kazuya Nishimura, Junya Hayashida, Chenyang Wang, Dai Fei Elmer Ker, Ryoma Bise
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.
3 code implementations • 27 Apr 2020 • Kazuya Nishimura, Ryoma Bise
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.
3 code implementations • CVPR 2020 • 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).
1 code implementation • 29 Nov 2019 • Kazuya Nishimura, Dai Fei Elmer Ker, Ryoma Bise
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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