6 citations · 24 across the 18 of their papers we have counts for
18 papers
Anti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning
Khashayar Namdar, Pin-Chien Wang, Tushar Raju +3
Anti-money laundering (AML) actions and measurements are among the priorities of financial institutions, for which machine learning (ML) has shown to have a high potential. In this…
AI-Driven MRI-based Brain Tumour Segmentation Benchmarking
Connor Ludwig, Khashayar Namdar, Farzad Khalvati
Medical image segmentation has greatly aided medical diagnosis, with U-Net based architectures and nnU-Net providing state-of-the-art performance. There have been numerous general…
Red Teaming Large Language Models for Healthcare
Vahid Balazadeh, Michael Cooper, David Pellow +32
We present the design process and findings of the pre-conference workshop at the Machine Learning for Healthcare Conference (2024) entitled Red Teaming Large Language Models for He…
Opportunities for Persian Digital Humanities Research with Artificial Intelligence Language Models; Case Study: Forough Farrokhzad
Arash Rasti Meymandi, Zahra Hosseini, Sina Davari +6
This study explores the integration of advanced Natural Language Processing (NLP) and Artificial Intelligence (AI) techniques to analyze and interpret Persian literature, focusing…
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…
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…
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…
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…
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…
A Modified AUC for Training Convolutional Neural Networks: Taking Confidence into Account
Khashayar Namdar, Masoom A. Haider, Farzad Khalvati
Receiver operating characteristic (ROC) curve is an informative tool in binary classification and Area Under ROC Curve (AUC) is a popular metric for reporting performance of binary…
A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks
Ruqian Hao, Khashayar Namdar, Lin Liu +2
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the t…
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…