Bogdan Georgescu | DeepAI

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Featured Co-authors

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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...

0 Manuela Daniela Danu, et al. ∙

∙ 01/04/2022

Self-supervised Learning from 100 Million Medical Images

Building accurate and robust artificial intelligence systems for medical...

462 Florin C. Ghesu, et al. ∙

∙ 07/08/2020

Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment

The interpretation of medical images is a challenging task, often compli...

27 Florin C. Ghesu, et al. ∙

∙ 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...

31 Bogdan Georgescu, et al. ∙

∙ 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 ...

18 Shikha Chaganti, et al. ∙

∙ 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...

28 Siqi Liu, et al. ∙

∙ 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...

12 Shikha Chaganti, et al. ∙

∙ 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...

6 Siqi Liu, et al. ∙

∙ 09/09/2019

Learning a Self-inverse Network for Unpaired Bidirectional Image-to-image Translation

Recently image-to-image translation has attracted significant interests ...

33 Zengming Shen, et al. ∙

∙ 09/09/2019

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...

18 Zengming Shen, et al. ∙

∙ 06/18/2019

Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment

The interpretation of chest radiographs is an essential task for the det...

2 Florin C. Ghesu, et al. ∙

∙ 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...

5 Sebastian Guendel, et al. ∙

∙ 03/29/2019

3D Organ Shape Reconstruction from Topogram Images

Automatic delineation and measurement of main organs such as liver is on...

0 Elena Balashova, et al. ∙

∙ 12/28/2018

Class-Aware Adversarial Lung Nodule Synthesis in CT Images

Though large-scale datasets are essential for training deep learning sys...

14 Jie Yang, et al. ∙

∙ 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 ...

4 Siqi Liu, et al. ∙

∙ 05/08/2018

Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation

Simultaneous segmentation of multiple organs from different medical imag...

2 Saeid Asgari Taghanaki, et al. ∙

∙ 04/14/2018

Select, Attend, and Transfer: Light, Learnable Skip Connections

Skip connections in deep networks have improved both segmentation and cl...

0 Saeid Asgari Taghanaki, et al. ∙

∙ 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...

0 Sebastian Guendel, et al. ∙

∙ 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...

0 Dong Yang, et al. ∙

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