Search Results for author: Geoffrey D. Rubin

Found 6 papers, 5 papers with code

Co-occurring Diseases Heavily Influence the Performance of Weakly Supervised Learning Models for Classification of Chest CT

no code implementations23 Feb 2022 Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin, Ehsan Samei, Joseph Y. Lo

Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD).

Binary Classification Classification +2

Classification of Multiple Diseases on Body CT Scans using Weakly Supervised Deep Learning

1 code implementation3 Aug 2020 Fakrul Islam Tushar, Vincent M. D'Anniballe, Rui Hou, Maciej A. Mazurowski, Wanyi Fu, Ehsan Samei, Geoffrey D. Rubin, Joseph Y. Lo

Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. Materials & Methods: This retrospective study included a total of 12, 092 patients (mean age 57 +- 18; 6, 172 women) for model development and testing (from 2012-2017).

Computed Tomography (CT) General Classification

Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes

1 code implementation12 Feb 2020 Rachel Lea Draelos, David Dov, Maciej A. Mazurowski, Joseph Y. Lo, Ricardo Henao, Geoffrey D. Rubin, Lawrence Carin

This model reached a classification performance of AUROC greater than 0. 90 for 18 abnormalities, with an average AUROC of 0. 773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data.

BIG-bench Machine Learning Computed Tomography (CT) +1

Cannot find the paper you are looking for? You can Submit a new open access paper.