Search Results for author: Jameson Merkow

Found 6 papers, 3 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

3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology

1 code implementation23 Nov 2023 Asma Ben Abacha, Alberto Santamaria-Pang, Ho Hin Lee, Jameson Merkow, Qin Cai, Surya Teja Devarakonda, Abdullah Islam, Julia Gong, Matthew P. Lungren, Thomas Lin, Noel C Codella, Ivan Tarapov

The increasing use of medical imaging in healthcare settings presents a significant challenge due to the increasing workload for radiologists, yet it also offers opportunity for enhancing healthcare outcomes if effectively leveraged.

Medical Image Retrieval Retrieval

Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query

no code implementations9 May 2023 Ho Hin Lee, Alberto Santamaria-Pang, Jameson Merkow, Ozan Oktay, Fernando Pérez-García, Javier Alvarez-Valle, Ivan Tarapov

We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) that demonstrates the feasibility of medical image retrieval with similar anatomical regions.

Anatomy Contrastive Learning +2

CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AI

1 code implementation6 Feb 2022 Arjun Soin, Jameson Merkow, Jin Long, Joseph Paul Cohen, Smitha Saligrama, Stephen Kaiser, Steven Borg, Ivan Tarapov, Matthew P Lungren

We use the CheXpert and PadChest public datasets to build and test a medical imaging AI drift monitoring workflow to track data and model drift without contemporaneous ground truth.

DeepRadiologyNet: Radiologist Level Pathology Detection in CT Head Images

no code implementations26 Nov 2017 Jameson Merkow, Robert Lufkin, Kim Nguyen, Stefano Soatto, Zhuowen Tu, Andrea Vedaldi

Thus, DeepRadiologyNet enables significant reduction in the workload of human radiologists by automatically filtering studies and reporting on the high-confidence ones at an operating point well below the literal error rate for US Board Certified radiologists, estimated at 0. 82%.

Dense Volume-to-Volume Vascular Boundary Detection

1 code implementation26 May 2016 Jameson Merkow, David Kriegman, Alison Marsden, Zhuowen Tu

In this work, we present a novel 3D-Convolutional Neural Network (CNN) architecture called I2I-3D that predicts boundary location in volumetric data.

Boundary Detection

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