211 citations · 415 across the 41 of their papers we have counts for
20 papers · 1 filter
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks
Meng Zhou, Matthias W Wagner, Uri Tabori +3
Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating real…
Using Large Text-to-Image Models with Structured Prompts for Skin Disease Identification: A Case Study
Sajith Rajapaksa, Jean Marie Uwabeza Vianney, Renell Castro +2
This paper investigates the potential usage of large text-to-image (LTI) models for the automated diagnosis of a few skin conditions with rarity or a serious lack of annotated data…
Non-invasive Liver Fibrosis Screening on CT Images using Radiomics
Jay J. Yoo, Khashayar Namdar, Sean Carey +4
Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver. Methods: For this retrospective, single-centre study, radiom…
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location
Khashayar Namdar, Matthias W. Wagner, Kareem Kudus +4
Background and Purpose: Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, and identification of molecular markers for pLGG is crucial for succes…
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images
Jay J. Yoo, Khashayar Namdar, Farzad Khalvati
Training machine learning models to segment tumors and other anomalies in medical images is an important step for developing diagnostic tools but generally requires manually annota…
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning
Khashayar Namdar, Partoo Vafaeikia, Farzad Khalvati
Generalizability is the ultimate goal of Machine Learning (ML) image classifiers, for which noise and limited dataset size are among the major concerns. We tackle these challenges…
Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology
Pranav Agnihotri, Sara Ketabi, Khashayar +2
Traditional datasets for the radiological diagnosis tend to only provide the radiology image alongside the radiology report. However, radiology reading as performed by radiologists…
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification
Saman Motamed, Farzad Khalvati
Generative Adversarial Networks can learn the mapping of random noise to realistic images in a semi-supervised framework. This mapping ability can be used for semi-supervised image…
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification
Saman Motamed, Farzad Khalvati
From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. Wit…
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
Saman Motamed, Patrik Rogalla, Farzad Khalvati
Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques impr…
Prostate Cancer Detection using Deep Convolutional Neural Networks
Sunghwan Yoo, Isha Gujrathi, Masoom A. Haider +1
Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tool…
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
Xiaodan Hu, Audrey G. Chung, Paul Fieguth +3
Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms. Spec…
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields
Edward Li, Farzad Khalvati, Mohammad Javad Shafiee +2
Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for the screening and diagnosis of frequently occurring cancers. However image quality may suffer by long a…
Discovery Radiomics via StochasticNet Sequencers for Cancer Detection
Mohammad Javad Shafiee, Audrey G. Chung, Devinder Kumar +3
Radiomics has proven to be a powerful prognostic tool for cancer detection, and has previously been applied in lung, breast, prostate, and head-and-neck cancer studies with great s…
Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection
Audrey G. Chung, Mohammad Javad Shafiee, Devinder Kumar +3
Prostate cancer is the most diagnosed form of cancer in Canadian men, and is the third leading cause of cancer death. Despite these statistics, prognosis is relatively good with a…
Self-Configuring and Evolving Fuzzy Image Thresholding
A. Othman, H. R. Tizhoosh, F. Khalvati
Every segmentation algorithm has parameters that need to be adjusted in order to achieve good results. Evolving fuzzy systems for adjustment of segmentation parameters have been pr…
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
Zehra Camlica, H. R. Tizhoosh, Farzad Khalvati
Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literatur…
Discovery Radiomics for Pathologically-Proven Computed Tomography Lung Cancer Prediction
Devinder Kumar, Mohammad Javad Shafiee, Audrey G. Chung +3
Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater…
Autoencoding the Retrieval Relevance of Medical Images
Zehra Camlica, H. R. Tizhoosh, Farzad Khalvati
Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extract…
Evolving Fuzzy Image Segmentation with Self-Configuration
Ahmed Othman, Hamid R. Tizhoosh, Farzad Khalvati
Current image segmentation techniques usually require that the user tune several parameters in order to obtain maximum segmentation accuracy, a computationally inefficient approach…