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07/14/2023
ConTrack: Contextual Transformer for Device Tracking in X-ray
Device tracking is an important prerequisite for guidance during endovas...
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06/18/2023
Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge
Among all the sub-sections in a typical radiology report, the Clinical I...
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01/04/2022
Self-supervised Learning from 100 Million Medical Images
Building accurate and robust artificial intelligence systems for medical...
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08/13/2020
Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digita
Purpose: To leverage volumetric quantification of airspace disease (AD) ...
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07/08/2020
Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment
The interpretation of medical images is a challenging task, often compli...
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06/09/2020
Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort
Purpose: To investigate if AI-based classifiers can distinguish COVID-19...
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06/09/2020
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Purpose: To present a method that automatically segments and quantifies ...
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05/05/2020
3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
The Coronavirus Disease (COVID-19) has affected 1.8 million people and r...
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04/02/2020
Quantification of Tomographic Patterns associated with COVID-19 from Chest CT
Purpose: To present a method that automatically detects and quantifies a...
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03/18/2020
Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images
With the injection of contrast material into blood vessels, multi-phase ...
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03/08/2020
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks
Detecting malignant pulmonary nodules at an early stage can allow medica...
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06/18/2019
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
The interpretation of chest radiographs is an essential task for the det...
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05/15/2019
Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels
Chest X-ray (CXR) is the most common X-ray examination performed in dail...
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12/28/2018
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Though large-scale datasets are essential for training deep learning sys...
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12/04/2018
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition
The performance of medical image analysis systems is constrained by the ...
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05/08/2018
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
Simultaneous segmentation of multiple organs from different medical imag...
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05/01/2018
Generating Synthetic X-ray Images of a Person from the Surface Geometry
We present a novel framework that learns to predict human anatomy from b...
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04/14/2018
Select, Attend, and Transfer: Light, Learnable Skip Connections
Skip connections in deep networks have improved both segmentation and cl...
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03/12/2018
Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks
Chest X-ray is the most common medical imaging exam used to assess multi...
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11/23/2017
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
While deep convolutional neural networks (CNN) have been successfully ap...
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07/25/2017
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many c...
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05/17/2017
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
Automatic localization and labeling of vertebra in 3D medical images pla...
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03/19/2017
A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes
We propose a fully-automated method for accurate and robust detection an...
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11/30/2016
