8 citations · 17 across the 18 of their papers we have counts for
20 papers
Exploring the Latent Structure of AMD Severity Using Retinal Foundation Models
Niccolò Fineschi, Jovana Panic, Atsushi Watanabe +7
Retinal foundation models have demonstrated strong performance across a wide range of ophthalmic tasks, yet relatively little is known about how disease-related information is repr…
Preserving DEG Rankings for Gene Discovery in Histology-Based Spatial Gene Expression Prediction
Kaito Shiku, Kazuya Nishimura, Yasuhiro Kojima +1
Predicting spatial gene expression from histology images could scale spatial transcriptomics (ST) to image-only cohorts, but conventional histology-based ST prediction is trained a…
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
Kaito Shiku, Ahtisham Fazeel Abbasi, Ryoma Bise +4
Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images and cell locations, reducing…
Leveraging Cell-level Spatial Transcriptomics as Weak Supervision for Nuclei Classification
Kazuya Nishimura, Ryoma Bise, Haruka Hirose +1
Deep learning-based nuclei analysis in histopathology relies heavily on large-scale, expert-curated datasets that are costly and difficult to scale across organs. To address this,…
FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation
Yukinori Yamamoto, Kazuya Nishimura, Tsukasa Fukusato +3
Deep learning-based 3D medical image segmentation methods relies on large-scale labeled datasets, yet acquiring such data is difficult due to privacy constraints and the high cost…
Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images
Kazuya Nishimura, Ryoma Bise, Shinnosuke Matsuo +2
Estimating slide- and patch-level gene expression profiles from pathology images enables rapid and low-cost molecular analysis with broad clinical impact. Despite strong results, e…
Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
Kazuya Nishimura, Haruka Hirose, Ryoma Bise +2
Gene expression estimation from pathology images has the potential to reduce the RNA sequencing cost. Point-wise loss functions have been widely used to minimize the discrepancy be…
Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection
Kaito Shiku, Kazuya Nishimura, Shinnosuke Matsuo +2
Spatial transcriptomics (ST) is a novel technology that enables the observation of gene expression at the resolution of individual spots within pathological tissues. ST quantifies…
Towards Spatial Transcriptomics-guided Pathological Image Recognition with Batch-Agnostic Encoder
Kazuya Nishimura, Ryoma Bise, Yasuhiro Kojima
Spatial transcriptomics (ST) is a novel technique that simultaneously captures pathological images and gene expression profiling with spatial coordinates. Since ST is closely relat…
Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer
Kaito Shiku, Kazuya Nishimura, Daiki Suehiro +2
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. However, previous UC class…
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…
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…
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…
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…
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…