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Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment
The interpretation of medical images is a challenging task, often compli...
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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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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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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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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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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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Learning a Self-inverse Network for Unpaired Bidirectional Image-to-image Translation
Recently image-to-image translation has attracted significant interests ...
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Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation
The one-to-one mapping is necessary for many bidirectional image-to-imag...
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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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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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3D Organ Shape Reconstruction from Topogram Images
Automatic delineation and measurement of main organs such as liver is on...
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Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Though large-scale datasets are essential for training deep learning sys...
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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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Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
Simultaneous segmentation of multiple organs from different medical imag...
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Select, Attend, and Transfer: Light, Learnable Skip Connections
Skip connections in deep networks have improved both segmentation and cl...
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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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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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