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20172020
most citedEvaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge

29 citations · 55 across the 4 of their papers we have counts for

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7 papers · 1 filter

cs.CV2019

PAN: Projective Adversarial Network for Medical Image Segmentation

Naji Khosravan, Aliasghar Mortazi, Michael Wallace +1

Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D s…

cs.CV201929 cited

Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge

Xiahai Zhuang, Lei Li, Christian Payer +31

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable…

cs.CV2018

S4ND: Single-Shot Single-Scale Lung Nodule Detection

Naji Khosravan, Ulas Bagci

The state of the art lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge…

cs.CV2018

A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning

Naji Khosravan, Haydar Celik, Baris Turkbey +3

There are at least two categories of errors in radiology screening that can lead to suboptimal diagnostic decisions and interventions:(i)human fallibility and (ii)complexity of vis…

cs.CV2018

Semi-supervised multi-task learning for lung cancer diagnosis

Naji Khosravan, Ulas Bagci

Early detection of lung nodules is of great importance in lung cancer screening. Existing research recognizes the critical role played by CAD systems in early detection and diagnos…

cs.CV201713 cited

Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning

Dustin Morley, Hassan Foroosh, Saad Shaikh +1

We propose a new deep learning approach for automatic detection and segmentation of fluid within retinal OCT images. The proposed framework utilizes both ResNet and Encoder-Decoder…