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Suzuki Lab
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The lab builds AI that finds diseases in medical images to support physicians' diagnosis.
AboutAbout the lab
AI learns the diagnostic skills of physicians.
The Suzuki Lab researches computer-aided diagnosis using medical images, and machine learning for image processing and image analysis.
Its goal is to build AI that learns, from data and clinical cases, the skills physicians use to read images and make correct diagnoses.
The lab works on research in which AI finds lesions in images to assist physicians' diagnosis, and research in which AI learns to identify organs as experienced physicians do.
It is the Biomedical AI Research Unit of the Institute of Integrated Research, spanning computer engineering and the biomedical field.
Key terms
- Computer-aided diagnosis
- A mechanism in which a computer analyzes medical images, finds and distinguishes lesions, and helps physicians make a diagnosis.
- Medical image
- An image, such as a CT scan or X-ray radiograph, that shows the inside of the body and is used to diagnose disease.
- Neural network
- A computational mechanism modeled on the connections between neurons in the brain. It learns features from many examples.
NewsLab news
Prof. Suzuki won second place in the pitch at GTIE Demo Day 2026
Held an online lab information session and an open lab event
Prof. Suzuki won the NINEJP Pitch Grand Prize
Started joint research with a lab at Massachusetts General Hospital / Harvard Medical School
ResearchResearch themes
The lab aims to develop AI technology that learns, from data and clinical cases, the image recognition skills physicians use to make correct diagnoses. Targeting medical images, it works on computer-aided diagnosis and machine learning for image processing and image analysis.
*Illustrative imageAI-Assisted Diagnosis Systems
These are systems in which AI learns to find and diagnose lesions in medical images and supports physicians' diagnosis. The lab has also developed methods such as tracing blood vessels in coronary angiograms.
*Illustrative imageDeep Learning and Machine Learning
The lab develops neural networks that learn directly from images, such as MTANN. The research covers image noise reduction and reducing false positives when searching for lung nodules in low-dose CT.
*Illustrative imageAI Imaging
This research has AI learn image processing that separates bone from soft tissue in X-ray images. The lab has also developed technology that makes changes such as growing cancers easier to see in chest radiographs.
MembersFaculty
The Suzuki Lab's members include faculty, researchers, research and technical support staff, doctoral, master's, and undergraduate students, and domestic and international collaborators.As of 2026
- ProfessorKenji SuzukiPublications ↗
- Assistant ProfessorZe Jin
- Project ProfessorShigetoshi SugioPublications ↗
- Project ProfessorHiroyuki Yoshida
- Project Assistant ProfessorAyu Rahmania
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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: “Coronary CT Angiography Scan” Goleisureintl / CC BY 4.0 (cropped)
- AI-Assisted Diagnosis Systems: “Ha1” Bleiglass / CC BY-SA 3.0 (cropped)
- Deep Learning and Machine Learning: “CT of part solid lung nodule” Annemie Snoeckx, Pieter Reyntiens, Damien Desbuquoit ほか
（論文著者） / CC BY 4.0 (cropped) - AI Imaging: “Normal posteroanterior (PA) chest radiograph (X-ray)” Mikael Häggström / CC0 (cropped)
- Related lab (山口研究室): “Hologram by Ken Harris, 2012 Switzerland” Harris, Ken / CC BY-SA 4.0 (cropped)
- Related lab (スラヴァキス研究室): “The manifold paths of biology ( )” UCL Mathematical & Physical Sciences / CC BY 2.0 (cropped)
- Related lab (一色研究室): “Circuit image on Rubylith” Camera Operator: TSGT Bob Wickley / Public domain (cropped)
- Related lab (イスラム研究室): “Micromanipulator Semiautomatic Probe Station” Rgarrigan / CC BY-SA 4.0 (cropped)
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