OpenAlex · Aktualisierung stündlich · Letzte Aktualisierung: 20.03.2026, 00:46

Huazhong Agricultural University

59.032 Arbeiten6.149.353 Zitationen
Land: CNTyp: education

Relevante Arbeiten

Meistzitierte Publikationen im Bereich Gesundheit & MedTech

Innovative applications of artificial intelligence during the COVID-19 pandemic

Chenrui Lv, Wenqiang Guo, Xinyi Yin et al.

2024 · 48 Zit.

The Curative Power of Medical Data

Daniela Gîfu, Diana Trandabăţ, Kevin Bretonnel Cohen et al.

2018 · 4 Zit.

Real-time and accurate estimation of surgical hemoglobin loss using deep learning-based medical sponges image analysis

Kai Li, Zexin Cheng, Jun‐Jie Zeng et al.

2023 · 3 Zit.

supplementary file.xls

Dan Feng, Tianjiao Liu, Xin Li et al.

2023 · 1 Zit.

Applications of knowledge graph in medical and financial fields: Data integration and intelligent decision-making from an interdisciplinary perspective

Ruichen Xi

2024 · 1 Zit.

MEDA 2020

Kevin Bretonnel Cohen, Daniela Gîfu, Youzhu Li et al.

2020 · 0 Zit.

Additional file 3 of Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

Yan Xiang, Xurong Mou, Yanan Yang et al.

2023 · 0 Zit.

Additional file 4 of Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

Yan Xiang, Xurong Mou, Yanan Yang et al.

2023 · 0 Zit.

Additional file 2 of Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

Yan Xiang, Xurong Mou, Yanan Yang et al.

2023 · 0 Zit.

Learning fair representation for fine-tuning pre-trained language models

Ke Wang, Yao Zhang, Hong-Yu Zhang et al.

2026 · 0 Zit.

Additional file 5 of Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

Yan Xiang, Xurong Mou, Yanan Yang et al.

2023 · 0 Zit.

Letter to the Editor “Decoding the black box: critical appraisal of deep learning radiomics for predicting neoadjuvant response in head and neck cancer”

Qianyu Fan, Sheng Hu, Kangkang Ji

2025 · 0 Zit.