Search Results for author: Christopher P. Bridge

Found 10 papers, 4 papers with code

Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports

no code implementations19 Feb 2024 Felix J. Dorfner, Liv Jürgensen, Leonhard Donle, Fares Al Mohamad, Tobias R. Bodenmann, Mason C. Cleveland, Felix Busch, Lisa C. Adams, James Sato, Thomas Schultz, Albert E. Kim, Jameson Merkow, Keno K. Bressem, Christopher P. Bridge

While recent publications have explored GPT-4 in its application to extracting information of interest from radiology reports, there has not been a real-world comparison of GPT-4 to different leading open-source models.

Privacy Preserving

Improving the repeatability of deep learning models with Monte Carlo dropout

1 code implementation15 Feb 2022 Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Brian Befano, Silvia de Sanjosé, Diden Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer

During model development and evaluation, much attention is given to classification performance while model repeatability is rarely assessed, leading to the development of models that are unusable in clinical practice.

Attribute Binary Classification +6

Monte Carlo dropout increases model repeatability

1 code implementation12 Nov 2021 Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer

Leveraging Monte Carlo predictions significantly increased repeatability for all tasks on the binary, multi-class, and ordinal models leading to an average reduction of the 95% limits of agreement by 17% points.

Classification Density Estimation

Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video

no code implementations3 Jul 2017 Weilin Huang, Christopher P. Bridge, J. Alison Noble, Andrew Zisserman

We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart.

Introduction To The Monogenic Signal

1 code implementation27 Mar 2017 Christopher P. Bridge

The monogenic signal is an image analysis methodology that was introduced by Felsberg and Sommer in 2001 and has been employed for a variety of purposes in image processing and computer vision research.


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