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Georgy Gimel'farb

Researcher at University of Auckland

Publications -  312
Citations -  6139

Georgy Gimel'farb is an academic researcher from University of Auckland. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 34, co-authored 312 publications receiving 5461 citations. Previous affiliations of Georgy Gimel'farb include Russian Academy of Sciences & National Academy of Sciences of Ukraine.

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Journal ArticleDOI

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

Ayman El-Baz, +7 more
- 29 Jan 2013 - 
TL;DR: The paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.
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Models and methods for analyzing DCE-MRI: A review

Fahmi Khalifa, +7 more
- 01 Dec 2014 - 
TL;DR: Promising theoretical findings and experimental results suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.
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Precise segmentation of multimodal images

Aly A. Farag, +2 more
- 01 Apr 2006 - 
TL;DR: New techniques for unsupervised segmentation of multimodal grayscale images such that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of grey levels are proposed.
Posted Content

Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network

Ehsan Hosseini-Asl, +2 more
- 02 Jul 2016 - 
TL;DR: To predict the Alzheimer's disease with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets, which has been validated on the CADDementia dataset.
Journal ArticleDOI

Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network

Ehsan Hosseini-Asl, +9 more
- 01 Jan 2018 - 
TL;DR: Wang et al. as mentioned in this paper proposed a 3D-CNN model based on a convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans for source domain.