102 citations · 112 across the 5 of their papers we have counts for
5 papers
Co-occurring Diseases Heavily Influence the Performance of Weakly Supervised Learning Models for Classification of Chest CT
Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin +2
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 (CA…
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning
Vincent M. D'Anniballe, Fakrul Islam Tushar, Khrystyna Faryna +4
Purpose: To develop high throughput multi-label annotators for body (chest, abdomen, and pelvis) Computed Tomography (CT) reports that can be applied across a variety of abnormalit…
Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features
Anindo Saha, Fakrul I. Tushar, Khrystyna Faryna +5
Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of…
Classification of Multiple Diseases on Body CT Scans using Weakly Supervised Deep Learning
Fakrul Islam Tushar, Vincent M. D'Anniballe, Rui Hou +5
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports.Materials & Method…
Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes
Rachel Lea Draelos, David Dov, Maciej A. Mazurowski +4
Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data s…