8 citations · 27 across the 22 of their papers we have counts for
22 papers
VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images
Soichi Mita, Shumpei Takezaki, Ryoma Bise
Vessel centerline extraction from 3D CT images is an important task because it reduces annotation effort to build a model that estimates a vessel structure. It is challenging to es…
Guidance-base Diffusion Models for Improving Photoacoustic Image Quality
Tatsuhiro Eguchi, Shumpei Takezaki, Mihoko Shimano +2
Photoacoustic(PA) imaging is a non-destructive and non-invasive technology for visualizing minute blood vessel structures in the body using ultrasonic sensors. In PA imaging, the i…
Proportion Estimation by Masked Learning from Label Proportion
Takumi Okuo, Kazuya Nishimura, Hiroaki Ito +3
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. This metric is recorded as diagnostic i…
Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection
Kaito Shiku, Hiromitsu Shirai, Takeshi Ishihara +1
3D cell tracking in a living organism has a crucial role in live cell image analysis. Cell tracking in C. elegans has two difficulties. First, cell migration in a consecutive frame…
Counting Network for Learning from Majority Label
Kaito Shiku, Shinnosuke Matsuo, Daiki Suehiro +1
The paper proposes a novel problem in multi-class Multiple-Instance Learning (MIL) called Learning from the Majority Label (LML). In LML, the majority class of instances in a bag i…
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions
Takanori Asanomi, Shinnosuke Matsuo, Daiki Suehiro +1
Learning from label proportions (LLP) is a promising weakly supervised learning problem. In LLP, a set of instances (bag) has label proportions, but no instance-level labels are gi…
Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping
Kazuya Nishimura, Ami Katanaya, Shinichiro Chuma +1
Detection of mitosis events plays an important role in biomedical research. Deep-learning-based mitosis detection methods have achieved outstanding performance with a certain amoun…
Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
Xiaoqing Liu, Kengo Araki, Shota Harada +7
The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the targe…
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation
Xiaoqing Liu, Kenji Ono, Ryoma Bise
The development of medical image segmentation using deep learning can significantly support doctors' diagnoses. Deep learning needs large amounts of data for training, which also r…
Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection
Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe +1
Cell detection is an important task in biomedical research. Recently, deep learning methods have made it possible to improve the performance of cell detection. However, a detection…
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
Shota Harada, Ryoma Bise, Kengo Araki +7
Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a s…
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…
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…
Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response
Kazuya Nishimura, Dai Fei Elmer Ker, Ryoma Bise
Cell shape analysis is important in biomedical research. Deep learning methods may perform to segment individual cells if they use sufficient training data that the boundary of eac…
Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology
Hiroki Tokunaga, Yuki Teramoto, Akihiko Yoshizawa +1
Automated digital histopathology image segmentation is an important task to help pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer subtypes, pa…
Virtual Blood Vessels in Complex Background using Stereo X-ray Images
Qiuyu Chen, Ryoma Bise, Lin Gu +5
We propose a fully automatic system to reconstruct and visualize 3D blood vessels in Augmented Reality (AR) system from stereo X-ray images with bones and body fat. Currently, typi…