Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Kenji Suzuki

Professor

Tokyo Institute of Technology

Japan

Research Interests

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Explore related searches

Professors in Japan Professors in Medical Imaging Medical Imaging professors in Japan

About

Kenji Suzuki is a Professor at the Tokyo Institute of Technology in Japan. His research areas include medical imaging, artificial intelligence, and machine learning applications in medicine, as demonstrated by his recent articles on topics such as anomaly detection, federated learning, and automated measurement techniques. He focuses on improving diagnostic processes and outcomes through innovative AI solutions in medical contexts.

Articles (21)

Year:

2026

Year:

2026

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Collaborators (5)

Wahyu Rahmaniar

Assistant Professor (specially appointed)

Tokyo Institute of Technology

JAPAN

View details

Tomoyuki Shishido

Chief Technology Officer

JAPAN

View details

Motohiro Fujiwara

Memorial Sloan Kettering Cancer Center

UNITED STATES

View details

Masayuki Ikebe

Hokkaido University

JAPAN

View details

Masahiro Yamaguchi

Tokyo Institute of Technology

JAPAN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Kenji Suzuki

Research Interests

Make your next move.

Explore related searches

About

Articles (21)

Multi-AD: cross-domain unsupervised anomaly detection for medical and industrial applications

Computer-Aided Diagnosis and Monitoring of Rheumatoid Arthritis in Conventional Radiography: Advancements and Future Opportunities

Future of the Medical World: Collaborative Medical Imaging AI with Federated Learning

Patch-Based Deep-Learning Model With Limited Training Dataset for Liver Tumor Segmentation in Contrast-Enhanced Hepatic Computed Tomography

Quantification-based explainable artificial intelligence for deep learning decisions: clustering and visualization of quantitative morphometric features in hepatocellular carcinoma discrimination

Layer Separation: Towards Adjustable Joint Space Width Images Synthesis

Federated Active Learning Framework for Efficient Annotation Strategy in Skin-Lesion Classification

AI and machine learning in medical imaging: key points from development to translation

Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing

Auto-CA: Automated Cobb Angle Measurement Based on Vertebrae Detection for Assessment of Spinal Curvature Deformity

Artificial intelligence model substantially improves stratum corneum moisture content prediction from visible‐light skin images and skin feature factors

Urinary Stones Segmentation in Abdominal X-Ray Images Using Cascaded U-Net Pipeline with Stone-Embedding Augmentation and Lesion-Size Reweighting Approach

Urinary Stones Segmentation in Abdominal X-Ray Images Using Cascaded U-Net Pipeline with Stone-Embedding Augmentation and Lesion-Size Reweighting Approach

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging

Auto-CA: Automated Cobb Angle Measurement Based on Vertebrae Detection for Assessment of Spinal Curvature Deformity

Improving Kidney Tumor Classification With Multi-Modal Medical Images Recovered Partially by Conditional CycleGAN

Improvement of Urinary Stone Segmentation Using GAN-Based Urinary Stones Inpainting Augmentation

Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray

Artificial Intelligence for Virtual Medical Imaging for Accurate Diagnosis

Prediction of genetically-evaluated tumour responses to chemotherapy from breast MRI using machine learning with model selection

Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

Collaborators (5)

Wahyu Rahmaniar

Tomoyuki Shishido

Motohiro Fujiwara

Masayuki Ikebe

Masahiro Yamaguchi

Make your next move.