Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules - Top-Institutions - MDSpire

Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules

By
Renate Dinnessen
Dré Peeters
Noa Antonissen
Firdaus A. A. Mohamed Hoesein
Hester A. Gietema
Ernst Th. Scholten
Cornelia Schaefer-Prokop
Colin Jacobs
July 15, 2025
0 min

European Radiology

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Top Institutions in Radiology

Brief introduction explaining scope and methodology.

#1

Massachusetts General Hospital

Boston, MA
Key Differentiators
- Pulmonary Medicine
- Radiology
- Artificial Intelligence
#2

Memorial Sloan Kettering Cancer Center

New York, NY
Key Differentiators
- Thoracic Oncology
- Radiology
- Machine Learning
#3

University of California, San Francisco

San Francisco, CA
Key Differentiators
- Pulmonary Medicine
- Radiology
- Biomedical Informatics
#4

Radboud University Medical Center

Nijmegen, Netherlands
Key Differentiators
- Pulmonary Medicine
- Radiology
- Artificial Intelligence
#5

University of Toronto

Toronto, Ontario
Key Differentiators
- Pulmonary Medicine
- Radiology
- Data Science

Original Source(s)

European Radiology

Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules

by Renate Dinnessen, Dré Peeters, Noa Antonissen, Firdaus A. A. Mohamed Hoesein, Hester A. Gietema, Ernst Th. Scholten, Cornelia Schaefer-Prokop, Colin Jacobs
July 15, 2025

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