77 citations
- Harvard UniversityUS11 papers
- Brigham and Women's HospitalUS9 papers
- Harvard University PressUS7 papers
- Maastricht UniversityNL7 papers
- Dana-Farber Brigham Cancer CenterUS5 papers
- Charité - Universitätsmedizin BerlinDE4 papers
- Dana-Farber Cancer InstituteUS4 papers
- Humboldt-Universität zu BerlinDE4 papers
- Massachusetts Institute of TechnologyUS4 papers
- Technical University of MunichDE4 papers
- Boston Children's HospitalUS3 papers
- Cornell UniversityUS3 papers
4 papers · 1 filter
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
Felix J. Dorfner, Janis L. Vahldiek, Leonhard Donle +15
Purpose: To examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression. Methods: This r…
FIESTA: Autoencoders for accurate fiber segmentation in tractography
Félix Dumais, Jon Haitz Legarreta, Carl Lemaire +7
White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and a…
What Does DALL-E 2 Know About Radiology?
Lisa C. Adams, Felix Busch, Daniel Truhn +3
Generative models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation for artificial intelligence research in radiology pr…
Monte Carlo dropout increases model repeatability
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge +5
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Among the main features of robustness is repeatability. Much attention is gi…