no code implementations • 8 Aug 2020 • Aditya Sriram, Shivam Kalra, Morteza Babaie, Brady Kieffer, Waddah Al Drobi, Shahryar Rahnamayan, Hany Kashani, Hamid. R. Tizhoosh
In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images.
no code implementations • 30 Jul 2020 • Morteza Babaie, Hany Kashani, Meghana D. Kumar, Hamid. R. Tizhoosh
Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems.
no code implementations • 16 Apr 2020 • Mohammed Adnan, Shivam Kalra, Hamid. R. Tizhoosh
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology.
no code implementations • 5 Mar 2019 • Shivam Kalra, Larry Li, Hamid. R. Tizhoosh
The results are encouraging in demonstrating the potential of machine learning methods for classification and encoding of pathology reports.
no code implementations • 3 May 2018 • Taha J. Alhindi, Shivam Kalra, Ka Hin Ng, Anika Afrin, Hamid. R. Tizhoosh
In the present study, comparison of three classification models is conducted using features extracted using local binary patterns, the histogram of gradients, and a pre-trained deep network.
no code implementations • 8 Sep 2017 • Hojjat Salehinejad, Shahryar Rahnamayan, Hamid. R. Tizhoosh
Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE).
no code implementations • 13 Jan 2017 • Mehrdad J. Gangeh, Hamid. R. Tizhoosh, Kan Wu, Dun Huang, Hadi Tadayyon, Gregory J. Czarnota
One of the earliest steps in using QUS methods is contouring a region of interest (ROI) inside the tumour in ultrasound B-mode images.
no code implementations • 16 Sep 2016 • Hamid. R. Tizhoosh, Christopher Mitcheltree, Shujin Zhu, Shamak Dutta
Using images in a training dataset, we autoencode Radon projections to perform binarization on outputs of hidden layers.
no code implementations • 22 May 2016 • Fares Al-Qunaieer, Hamid. R. Tizhoosh, Shahryar Rahnamayan
This paper introduces a framework for the automated selection of the best resolution for image segmentation.
no code implementations • 14 May 2016 • Mina Nouredanesh, Hamid. R. Tizhoosh, Ershad Banijamali
This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side.
no code implementations • 24 Apr 2016 • Antonio Sze-To, Hamid. R. Tizhoosh, Andrew K. C. Wong
In this study, we explored using a deep de-noising autoencoder (DDA), with a new unsupervised training scheme using only backpropagation and dropout, to hash images into binary codes.
no code implementations • 16 Apr 2016 • Hamid. R. Tizhoosh, Ahmed A. Othman
Quantifying the accuracy of segmentation and manual delineation of organs, tissue types and tumors in medical images is a necessary measurement that suffers from multiple problems.
no code implementations • 16 Apr 2016 • Hamid. R. Tizhoosh, Shahryar Rahnamayan
A small number of equidistant projections, e. g., 4 or 8, is generally used to generate short barcodes.
no code implementations • 16 Apr 2016 • Xinran Liu, Hamid. R. Tizhoosh, Jonathan Kofman
The present work introduces a new image retrieval method for medical applications that employs a convolutional neural network (CNN) with recently introduced Radon barcodes.
no code implementations • 8 Feb 2016 • Hamid. R. Tizhoosh, Mehrdad J. Gangeh, Hadi Tadayyon, Gregory J. Czarnota
Quantitative ultrasound (QUS) methods provide a promising framework that can non-invasively and inexpensively be used to predict or assess the tumour response to cancer treatment.
no code implementations • 25 Dec 2015 • Hojjat Salehinejad, Shahryar Rahnamayan, Hamid. R. Tizhoosh
Furthermore, comprehensive comparative simulations and analysis on performance of the MDE algorithms over various mutation schemes, population sizes, problem types (i. e. uni-modal, multi-modal, and composite), problem dimensionalities, and mutation factor ranges are conducted by considering population diversity analysis for stagnation and trapping in local optimum situations.
1 code implementation • 19 May 2015 • Hamid. R. Tizhoosh
This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types.
no code implementations • 23 Apr 2015 • Ahmed Othman, Hamid. R. Tizhoosh, Farzad Khalvati
However, EFIS suffers from a few limitations when used in practice mainly due to some fixed parameters.
no code implementations • 21 Apr 2015 • Hamid. R. Tizhoosh, Shahryar Rahnamayan
This, of course, is a very naive estimate of the actual or true (non-linear) opposite $\breve{x}_{II}$, which has been called type-II opposite in literature.