output
20192025
most citedWhat Does DALL-E 2 Know About Radiology?

77 citations

Showing 2021Show all

5 papers · 1 filter

cs.CV2021★ 1 cited

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…

eess.IV2021

Transfer Learning from an Artificial Radiograph-landmark Dataset for Registration of the Anatomic Skull Model to Dual Fluoroscopic X-ray Images

Chaochao Zhou, Thomas Cha, Yun Peng +1

Registration of 3D anatomic structures to their 2D dual fluoroscopic X-ray images is a widely used motion tracking technique. However, deep learning implementation is often impeded…

cs.CY2021★ 3 cited

The application of adaptive minimum match k-nearest neighbors to identify at-risk students in health professions education

Anshul Kumar, Taylor DiJohnson, Roger Edwards +1

Purpose: When a learner fails to reach a milestone, educators often wonder if there had been any warning signs that could have allowed them to intervene sooner. Machine learning ca…

eess.IV2021★ 27 cited

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

Christopher P. Bridge, Chris Gorman, Steven Pieper +6

Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperabil…

q-bio.TO2021★ 13 cited

Biomimetic peptide enriched nonwoven scaffolds promote calcium phosphate mineralisation

Robabeh Gharaei, Giuseppe Tronci, Parikshit Goswami +3

Cell-free translational strategies are needed to accelerate the repair of mineralised tissues, particularly large bone defects, using minimally invasive approaches. Regenerative bo…