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Found 14 papers, 0 papers with code
Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge
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.
Self-supervised Learning from 100 Million Medical Images
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.
You May Need both Good-GAN and Bad-GAN for Anomaly Detection
A Bad-GAN generates pseudo anomalies at the low-density area of inlier distribution, and thus the inlier/outlier distinction can be approximated.
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
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
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.
Communal Domain Learning for Registration in Drifted Image Spaces
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).
Region Proposal Networks with Contextual Selective Attention for Real-Time Organ Detection
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.
A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning
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.
Partitioned Shape Modeling with On-the-Fly Sparse Appearance Learning for Anterior Visual Pathway Segmentation
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
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.
Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation
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.
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
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.
A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm
Biofilm is a formation of microbial material on tooth substrata.
BiofilmQuant: A Computer-Assisted Tool for Dental Biofilm Quantification
Dental biofilm is the deposition of microbial material over a tooth substratum.
