108 citations · 249 across the 13 of their papers we have counts for
5 papers · 1 filter
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model
Mateusz Buda, Ashirbani Saha, Ruth Walsh +5
Breast cancer screening is one of the most common radiological tasks with over 39 million exams performed each year. While breast cancer screening has been one of the most studied…
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…
iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry
Wanyi Fu, Shobhit Sharma, Ehsan Abadi +6
Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical imag…
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…