Search Results for author: Awais Mansoor

Found 13 papers, 0 papers with code

Self-supervised Learning from 100 Million Medical Images

no code implementations4 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.

Computed Tomography (CT) Contrastive Learning +1

You May Need both Good-GAN and Bad-GAN for Anomaly Detection

no code implementations29 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.

Anatomy Anomaly Detection

Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth

no code implementations13 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.

Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment

no code implementations8 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.

Anatomy Classification +1

Communal Domain Learning for Registration in Drifted Image Spaces

no code implementations20 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).

Representation Learning

Region Proposal Networks with Contextual Selective Attention for Real-Time Organ Detection

no code implementations26 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.

object-detection Object Detection +2

A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning

no code implementations11 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.

Capacity Estimation Object Localization +1

Partitioned Shape Modeling with On-the-Fly Sparse Appearance Learning for Anterior Visual Pathway Segmentation

no code implementations5 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.

Optimally Stabilized PET Image Denoising Using Trilateral Filtering

no code implementations11 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.

Image Denoising

CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans

no code implementations11 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.

Computed Tomography (CT) Semantic Segmentation

Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation

no code implementations11 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.

Computed Tomography (CT) General Classification +1

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