Hirosaki University

High‐quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X‐ray diagnosis

Joe Hasei, Ryuichi Nakahara, Yujiro Otsuka et al.

2024 · 18 Zit.

The efficacy of acute normovolemic hemodilution for preventing perioperative allogeneic blood transfusion in gynecological cancer patients

Junichi Saito, Kenichi Masui, Satoko Noguchi et al.

2019 · 18 Zit.

The Three-Class Annotation Method Improves the AI Detection of Early-Stage Osteosarcoma on Plain Radiographs: A Novel Approach for Rare Cancer Diagnosis

Joe Hasei, Ryuichi Nakahara, Yujiro Otsuka et al.

2024 · 7 Zit.

Developing competencies relating to information science and technology in Japanese undergraduate medical education

Takeshi Onoue, Yoshikazu Asada, Rintarō Imafuku et al.

2024 · 5 Zit.

Trends and Future Prospects of AI Technology Utilization Research in JSRT　 —Challenges to Tackle and the Expected Role of Our Academic Society !?—

Ikuo Kawashita, Norio Hayashi, Masahide Tominaga et al.

2023 · 0 Zit.

Evaluation of Surgical Skills Using Machine Learning and Interpretation of Results with Explainable AI in Practical Laparoscopic Surgery Training

Lingbo YAN, 崇重 安部, Koki Ebina et al.

2026 · 0 Zit.

Hirosaki team at the NTCIR-18 RadNLP2024 Shared Task: Few-Shot Learning and Prompt Engineering for TNM Staging Classification of English Radiology Reports Using Large Language Models.

Ryutaro Mori, Koichi Okuda, Shota Hosokawa et al.

2025 · 0 Zit.