6 citations · 7 across the 7 of their papers we have counts for
7 papers
Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographs
Tomohiro Kikuchi, Kohei Yamamoto, Yukihiro Nomura +6
Purpose: To develop and validate a deep learning ensemble for estimating adult sex, age, height, and weight from coronal digitally reconstructed radiographs (DRRs) generated from d…
Large Language Model-Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset
Yosuke Yamagishi, Atsushi Takamatsu, Mototsugu Sato +4
Purpose: To evaluate whether large language model (LLM)-assisted label cleaning can identify label-report discordance in CT-RATE, a large-scale public chest CT dataset. Materials a…
Blinded Radiologist and LLM-Based Evaluation of LLM-Generated Japanese Translations of Chest CT Reports: Comparative Study
Yosuke Yamagishi, Atsushi Takamatsu, Yasunori Hamaguchi +4
Background: Accurate translation of radiology reports is important for multilingual research, clinical communication, and radiology education, but the validity of LLM-based evaluat…
ModernBERT is More Efficient than Conventional BERT for Chest CT Findings Classification in Japanese Radiology Reports
Yosuke Yamagishi, Tomohiro Kikuchi, Shouhei Hanaoka +2
Japanese language models for medical text classification face challenges with complex vocabulary and linguistic structures in radiology reports. This study compared three Japanese…
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model
Yosuke Yamagishi, Yuta Nakamura, Tomohiro Kikuchi +7
Background: Recent advances in large language models highlight the need for high-quality multilingual medical datasets. While Japan leads globally in CT scanner deployment and util…
Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging
Yosuke Yamagishi, Shouhei Hanaoka, Tomohiro Kikuchi +6
Objectives: To evaluate the zero-shot performance of Segment Anything Model 2 (SAM 2) in 3D segmentation of abdominal organs in CT scans, and to investigate the effects of prompt s…