-
Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment
The interpretation of medical images is a challenging task, often compli...
07/08/2020 ∙ by Florin C. Ghesu, et al. ∙
27
∙
share
read it
-
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...
06/09/2020 ∙ by Bogdan Georgescu, et al. ∙
31
∙
share
read it
-
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Purpose: To present a method that automatically segments and quantifies ...
06/09/2020 ∙ by Shikha Chaganti, et al. ∙
18
∙
share
read it
-
3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
The Coronavirus Disease (COVID-19) has affected 1.8 million people and r...
05/05/2020 ∙ by Siqi Liu, et al. ∙
28
∙
share
read it
-
Quantification of Tomographic Patterns associated with COVID-19 from Chest CT
Purpose: To present a method that automatically detects and quantifies a...
04/02/2020 ∙ by Shikha Chaganti, et al. ∙
12
∙
share
read it
-
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...
03/08/2020 ∙ by Siqi Liu, et al. ∙
6
∙
share
read it
-
Learning a Self-inverse Network for Unpaired Bidirectional Image-to-image Translation
Recently image-to-image translation has attracted significant interests ...
09/09/2019 ∙ by Zengming Shen, et al. ∙
33
∙
share
read it
-
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...
09/09/2019 ∙ by Zengming Shen, et al. ∙
18
∙
share
read it
-
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
The interpretation of chest radiographs is an essential task for the det...
06/18/2019 ∙ by Florin C. Ghesu, et al. ∙
2
∙
share
read it
-
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...
05/15/2019 ∙ by Sebastian Guendel, et al. ∙
4
∙
share
read it
-
3D Organ Shape Reconstruction from Topogram Images
Automatic delineation and measurement of main organs such as liver is on...
03/29/2019 ∙ by Elena Balashova, et al. ∙
0
∙
share
read it
-
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Though large-scale datasets are essential for training deep learning sys...
12/28/2018 ∙ by Jie Yang, et al. ∙
14
∙
share
read it
-
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 ...
12/04/2018 ∙ by Siqi Liu, et al. ∙
4
∙
share
read it
-
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
Simultaneous segmentation of multiple organs from different medical imag...
05/08/2018 ∙ by Saeid Asgari Taghanaki, et al. ∙
2
∙
share
read it
-
Select, Attend, and Transfer: Light, Learnable Skip Connections
Skip connections in deep networks have improved both segmentation and cl...
04/14/2018 ∙ by Saeid Asgari Taghanaki, et al. ∙
0
∙
share
read it
-
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...
03/12/2018 ∙ by Sebastian Guendel, et al. ∙
0
∙
share
read it
-
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many c...
07/25/2017 ∙ by Dong Yang, et al. ∙
0
∙
share
read it
