211 citations · 415 across the 41 of their papers we have counts for
14 papers · 1 filter
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…
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…
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 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…
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…
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…
Evolution-based Fine-tuning of CNNs for Prostate Cancer Detection
Khashayar Namdar, Isha Gujrathi, Masoom A. Haider +1
Convolutional Neural Networks (CNNs) have been used for automated detection of prostate cancer where Area Under Receiver Operating Characteristic (ROC) curve (AUC) is usually used…
A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI
Saman Motamed, Isha Gujrathi, Dominik Deniffel +3
The segmentation of prostate whole gland and transition zone in Diffusion Weighted MRI (DWI) are the first step in designing computer-aided detection algorithms for prostate cancer…