no code implementations • 18 Jun 2023 • Manuela Daniela Danu, George Marica, Sanjeev Kumar Karn, Bogdan Georgescu, Awais Mansoor, Florin Ghesu, Lucian Mihai Itu, Constantin Suciu, Sasa Grbic, Oladimeji Farri, Dorin Comaniciu
Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient.
no code implementations • 4 Jan 2022 • Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Dominik Neumann, Pragneshkumar Patel, R. S. Vishwanath, James M. Balter, Yue Cao, Sasa Grbic, Dorin Comaniciu
Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the creation of large and curated sets of annotated training examples.
no code implementations • 29 Sep 2021 • Riqiang Gao, Zhoubing Xu, Guillaume Chabin, Awais Mansoor, Florin-Cristian Ghesu, Bogdan Georgescu, Bennett A. Landman, Sasa Grbic
A Bad-GAN generates pseudo anomalies at the low-density area of inlier distribution, and thus the inlier/outlier distinction can be approximated.
no code implementations • 13 Aug 2020 • Eduardo Mortani Barbosa Jr., Warren B. Gefter, Rochelle Yang, Florin C. Ghesu, Si-Qi Liu, Boris Mailhe, Awais Mansoor, Sasa Grbic, Sebastian Piat, Guillaume Chabin, Vishwanath R S., Abishek Balachandran, Sebastian Vogt, Valentin Ziebandt, Steffen Kappler, Dorin Comaniciu
Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19.
no code implementations • 8 Jul 2020 • Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Eli Gibson, R. S. Vishwanath, Abishek Balachandran, James M. Balter, Yue Cao, Ramandeep Singh, Subba R. Digumarthy, Mannudeep K. Kalra, Sasa Grbic, Dorin Comaniciu
In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e. g., by 8% to 0. 91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs.
no code implementations • 20 Aug 2019 • Awais Mansoor, Marius George Linguraru
Designing a registration framework for images that do not share the same probability distribution is a major challenge in modern image analytics yet trivial task for the human visual system (HVS).
no code implementations • 26 Dec 2018 • Awais Mansoor, Antonio R. Porras, Marius George Linguraru
Our novel selective attention approach (i) effectively shrinks the search space inside the feature map, (ii) appends useful localization information to the hypothesized proposal for the detection architecture to learn where to look for each organ, and (iii) modifies the pyramid of regression references in the RPN by incorporating organ- and modality-specific information, which results in additional time reduction.
no code implementations • 11 Jul 2018 • Awais Mansoor, Juan J. Cerrolaza, Geovanny Perez, Elijah Biggs, Kazunori Okada, Gustavo Nino, Marius George Linguraru
The main contributions of our work are: (1) a generic lung field segmentation framework from CXR accommodating large shape variation for adult and pediatric cohorts; (2) a deep representation learning detection mechanism, \emph{ensemble space learning}, for robust object localization; and (3) \emph{marginal shape deep learning} for the shape deformation parameter estimation.
no code implementations • 5 Aug 2015 • Awais Mansoor, Juan J. Cerrolaza, Robert A. Avery, Marius G. Linguraru
In this work, we propose a partitioned joint statistical shape model approach with sparse appearance learning for the segmentation of healthy and pathological AVP.
no code implementations • 11 Jul 2014 • Awais Mansoor, Ulas Bagci, Daniel J. Mollura
Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative image analysis tasks.
no code implementations • 11 Jul 2014 • Awais Mansoor, Ulas Bagci, Brent Foster, Ziyue Xu, Deborah Douglas, Jeffrey M. Solomon, Jayaram K. Udupa, Daniel J. Mollura
Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies.
no code implementations • 11 Jul 2014 • Awais Mansoor, Ulas Bagci, Daniel J. Mollura
In this paper, we present a novel approach for fast, accurate, reliable segmentation of pathological lungs from CT scans by combining region-based segmentation method with local descriptor classification that is performed on an optimized sampling grid.
no code implementations • 9 Jul 2014 • Awais Mansoor, Valery Patsekin, Dale Scherl, J. Paul Robinson, Bartlomiej Rajwa
Biofilm is a formation of microbial material on tooth substrata.
no code implementations • 3 Jul 2014 • Awais Mansoor, Valery Patsekin, Dale Scherl, J. Paul Robinson, Bartlomiej Rajwa
Dental biofilm is the deposition of microbial material over a tooth substratum.