211 citations · 415 across the 41 of their papers we have counts for
44 papers
Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion
Meng Zhou, Yuxuan Zhang, Xiaolan Xu +2
Multimodal medical image fusion is a crucial task that combines complementary information from different imaging modalities into a unified representation, thereby enhancing diagnos…
Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks
Khashayar Namdar, Matthias W. Wagner, Cynthia Hawkins +3
Pediatric Low-Grade Neuroepithelial Tumors (PLGNT) are the most common pediatric cancer type, accounting for 40% of brain tumors in children, and identifying PLGNT molecular subtyp…
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…
Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation
Chaojun Chen, Khashayar Namdar, Yujie Wu +4
Scoliosis is a three-dimensional deformity of the spine, most often diagnosed in childhood. It affects 2-3% of the population, which is approximately seven million people in North…
Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models
Sajith Rajapaksa, Farzad Khalvati
Deep learning techniques have greatly benefited computer-aided diagnostic systems. However, unlike other fields, in medical imaging, acquiring large fine-grained annotated datasets…
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…
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images
Jay J. Yoo, Khashayar Namdar, Matthias W. Wagner +5
Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging. Using machine learning for this problem generally requires manuall…
Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input
Zilun Zhang, Farzad Khalvati
Many high-performance classification models utilize complex CNN-based architectures for Alzheimer's Disease classification. We aim to investigate two relevant questions regarding c…
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…
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
Khashayar Namdar, Matthias W. Wagner, Birgit B. Ertl-Wagner +1
Background: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we…
Improving Disease Classification Performance and Explainability of Deep Learning Models in Radiology with Heatmap Generators
Akino Watanabe, Sara Ketabi, Khashayar +2
As deep learning is widely used in the radiology field, the explainability of such models is increasingly becoming essential to gain clinicians' trust when using the models for dia…
Exploring COVID-19 Related Stressors Using Topic Modeling
Yue Tong Leung, Farzad Khalvati
The COVID-19 pandemic has affected lives of people from different countries for almost two years. The changes on lifestyles due to the pandemic may cause psychosocial stressors for…
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning
Partoo Vafaeikia, Matthias W. Wagner, Uri Tabori +2
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While th…
Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours
Sajith Rajapaksa, Farzad Khalvati
Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipeline. However, training accurate and reliable CNN…
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…
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification
Ruqian Hao, Khashayar Namdar, Lin Liu +1
Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) a…
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
Saman Motamed, Patrik Rogalla, Farzad Khalvati
COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results…
Evaluating Knowledge Transfer in Neural Network for Medical Images
Sina Akbarian, Laleh Seyyed-Kalantari, Farzad Khalvati +1
Deep learning and knowledge transfer techniques have permeated the field of medical imaging and are considered as key approaches for revolutionizing diagnostic imaging practices. H…
A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning
Partoo Vafaeikia, Khashayar Namdar, Farzad Khalvati
Multi-task learning (MTL) optimizes several learning tasks simultaneously and leverages their shared information to improve generalization and the prediction of the model for each…