Search Results for author: Ehsan Samei

Found 6 papers, 1 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).

Classification Multi-Label Classification

iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

no code implementations20 Aug 2020 Wanyi Fu, Shobhit Sharma, Ehsan Abadi, Alexandros-Stavros Iliopoulos, Qi. Wang, Joseph Y. Lo, Xiaobai Sun, William P. Segars, Ehsan Samei

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 images.

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

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