Search Results for author: Vishwa S. Parekh

Found 7 papers, 1 papers with code

Multitask radiological modality invariant landmark localization using deep reinforcement learning

2 code implementations MIDL 2019 Vishwa S. Parekh, Alex E. Bocchieri, Vladimir Braverman, Michael A. Jacobs

As a result, to develop a radiological decision support system, it would need to be equipped with potentially hundreds of deep learning models with each model trained for a specific task or organ.

Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results

no code implementations1 Aug 2019 Alex E. Bocchieri, Vishwa S. Parekh, Kathryn R. Wagner. Shivani Ahlawat, Vladimir Braverman, Doris G. Leung, Michael A. Jacobs

A current clinical challenge is identifying limb girdle muscular dystrophy 2I(LGMD2I)tissue changes in the thighs, in particular, separating fat, fat-infiltrated muscle, and muscle tissue.

Radiomic Synthesis Using Deep Convolutional Neural Networks

no code implementations25 Oct 2018 Vishwa S. Parekh, Michael A. Jacobs

Radiomics is a rapidly growing field that deals with modeling the textural information present in the different tissues of interest for clinical decision support.

MPRAD: A Multiparametric Radiomics Framework

no code implementations25 Sep 2018 Vishwa S. Parekh, Michael A. Jacobs

The use of radiomics for quantitative extraction of textural features from radiological imaging is increasing moving towards clinical decision support.

Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results

no code implementations10 Feb 2018 Vishwa S. Parekh, Katarzyna J. Macura, Susan Harvey, Ihab Kamel, Riham EI-Khouli, David A. Bluemke, Michael A. Jacobs

For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images.

Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results

no code implementations13 Jun 2016 Vishwa S. Parekh, Jeremy R. Jacobs, Michael A. Jacobs

On analyzing the performance of these methods, we observed that there was a high of similarity between multiparametric embedded images from NLDR methods and the ADC map and perfusion map.

Dimensionality Reduction

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