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Nagahara Lab
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The lab aims to create “autonomous intelligence” through computer vision and machine learning.
AboutAbout the lab
Aiming for intelligence that can perceive, think, and act.
The Nagahara Lab (Intelligent Sensing Division, ISLab) conducts research toward “autonomous intelligence” that understands the meaning and circumstances of the outside world, makes decisions, and acts on people and the environment.
The lab works on technologies such as computer vision, computational photography, machine learning, pattern recognition, and natural language processing, as well as their applications.
The lab grew out of the Intelligent Information Infrastructure Research Division of the D3 Center at the University of Osaka, and it conducts joint research with a wide range of organizations inside and outside the university.
Since FY2020, the lab has been a cooperating division of the Graduate School of Information Science and Technology, where it supports research projects led by students themselves.
Key terms
- Computational photography
- Techniques that combine a camera's optics with computational processing to capture or create images
- Deep sensing
- An approach that optimizes sensing (measurement) and the processing that follows jointly, rather than separately
- Annotation
- The task of attaching correct labels or marks to data such as images
- Omics data
- Data obtained by comprehensively examining the molecules of living organisms, such as gene expression
NewsLab news
View More ↗ResearchResearch themes
The lab aims to realize “autonomous intelligence” capable of perception, thought, and action. It studies component technologies such as computer vision, computational photography, machine learning, pattern recognition, and natural language processing, along with their applications.
*Illustrative imageJoint Optimization of Sensing and Analysis
The lab generalizes deep sensing, which jointly optimizes sensing and processing, to develop new computational cameras. This international collaboration aims to achieve high accuracy, low data requirements, and low computational cost across diverse vision and AI applications, and it also seeks to strengthen research ties among Japan, the US, and Canada.
*Illustrative imageMedical Image Analysis with Few Annotations
The lab researches methods that use semi-supervised and weakly supervised learning to analyze medical images with high accuracy at low annotation cost.
*Illustrative imageIntegrated Analysis of Images and Omics Data
Using image analysis techniques and machine learning, the lab develops methods to jointly analyze image data, such as pathology and microscopy images, and omics data, such as gene expression. It aims to contribute to the understanding of diseases and to research on diagnosis and treatment.
*Illustrative imageCancer Diagnosis Support through 3D Image Recognition
Cancer cell diagnosis, which has relied on visual inspection, will be automated by measuring the 3D shapes of cells, building a database, and making diagnoses with machine learning. The aim is to develop a fast, high-accuracy system to support cancer cell diagnosis.
MembersFaculty
The members of the Nagahara Lab consist of faculty, researchers, visiting researchers, interns, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorHajime Nagahara
- Associate ProfessorHideaki Hayashi
- Associate ProfessorHiroaki Santo
- Assistant ProfessorKazuya Nishimura
- Project Associate ProfessorSatoshi Yagi
- Project Assistant ProfessorAtsushi Ashida
- Project Assistant ProfessorBilel Daoud
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Intersection over Union - object detection bounding boxes” Adrian Rosebrock / CC BY-SA 4.0 (cropped)
- Joint Optimization of Sensing and Analysis: “S4000 Lens Mechanism” Glosome / CC BY-SA 4.0 (cropped)
- Medical Image Analysis with Few Annotations: “Chest x-ray - posteroanterior view” O'Dea / CC BY-SA 3.0 (cropped)
- Integrated Analysis of Images and Omics Data: “Histology of Spleen” Bhavya akula / CC BY-SA 4.0 (cropped)
- Cancer Diagnosis Support through 3D Image Recognition: “Breast cancer cell (2)” Unknown photographer / Public domain (cropped)
- Related lab (Nakashima Lab): “Sample view of data collection process using artificial intelligence application” Captain Andrew M. Freeman, Air Force Institute of Technology / Air Force Research Laboratories (AFRL), Sensors, ATR, Target Recognition Branch. / Public domain (cropped)
- Related lab (Uranishi Lab): “ISS-50 Shane Kimbrough uses a virtual reality headset in the Destiny lab” NASA / Public domain (cropped)
- Related lab (Kawahara Lab): “Artificial Neural Network with Chip” mikemacmarketing / original posted on flickr Liam Huang / clipped and posted on flickr / CC BY 2.0 (cropped)
- Related lab (Senoo Lab): “Fluidigm C1 Single-Cell Auto Prep system” Konrad Förstner / CC BY 2.0 (cropped)
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