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Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images

Autor: Zhang, Donghao, Liu, Siqi, Chaganti, Shikha, Gibson, Eli, Xu, Zhoubing, Grbic, Sasa, Cai, Weidong, Comaniciu, Dorin

With the injection of contrast material into blood vessels, multi-phase contrasted CT images can enhance the visibility of vessel networks in the human body. Reconstructing the 3D geometric morphology of liver vessels from the contrasted CT images ca

Externí odkaz: http://arxiv.org/abs/2003.07999

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No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks

Autor: Liu, Siqi, Setio, Arnaud Arindra Adiyoso, Ghesu, Florin C., Gibson, Eli, Grbic, Sasa, Georgescu, Bogdan, Comaniciu, Dorin

Detecting malignant pulmonary nodules at an early stage can allow medical interventions which may increase the survival rate of lung cancer patients. Using computer vision techniques to detect nodules can improve the sensitivity and the speed of inte

Externí odkaz: http://arxiv.org/abs/2003.03824

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Conditional Segmentation in Lieu of Image Registration

Autor: Hu, Yipeng, Gibson, Eli, Barratt, Dean C., Emberton, Mark, Noble, J. Alison, Vercauteren, Tom

Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned. Current learning-based registration methods have adopted the s

Externí odkaz: http://arxiv.org/abs/1907.00438

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Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment

Autor: Ghesu, Florin C., Georgescu, Bogdan, Gibson, Eli, Guendel, Sebastian, Kalra, Mannudeep K., Singh, Ramandeep, Digumarthy, Subba R., Grbic, Sasa, Comaniciu, Dorin

The interpretation of chest radiographs is an essential task for the detection of thoracic diseases and abnormalities. However, it is a challenging problem with high inter-rater variability and inherent ambiguity due to inconclusive evidence in the d

Externí odkaz: http://arxiv.org/abs/1906.07775

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Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels

Autor: Guendel, Sebastian, Ghesu, Florin C., Grbic, Sasa, Gibson, Eli, Georgescu, Bogdan, Maier, Andreas, Comaniciu, Dorin

Chest X-ray (CXR) is the most common X-ray examination performed in daily clinical practice for the diagnosis of various heart and lung abnormalities. The large amount of data to be read and reported, with 100+ studies per day for a single radiologis

Externí odkaz: http://arxiv.org/abs/1905.06362

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Class-Aware Adversarial Lung Nodule Synthesis in CT Images

Autor: Yang, Jie, Liu, Siqi, Grbic, Sasa, Setio, Arnaud Arindra Adiyoso, Xu, Zhoubing, Gibson, Eli, Chabin, Guillaume, Georgescu, Bogdan, Laine, Andrew F., Comaniciu, Dorin

Though large-scale datasets are essential for training deep learning systems, it is expensive to scale up the collection of medical imaging datasets. Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribu

Externí odkaz: http://arxiv.org/abs/1812.11204

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Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition

Autor: Liu, Siqi, Gibson, Eli, Grbic, Sasa, Xu, Zhoubing, Setio, Arnaud Arindra Adiyoso, Yang, Jie, Georgescu, Bogdan, Comaniciu, Dorin

The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations. Such systems require data to be annotated by experts with years of training, especially when diagnostic decisions are involved. Such d

Externí odkaz: http://arxiv.org/abs/1812.01737

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