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20192025
most citedA Brief Review of Deep Multi-task Learning and Auxiliary Task Learning

6 citations · 24 across the 18 of their papers we have counts for

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cs.CV2025

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…

cs.CV2022★ 1 cited

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…

cs.CV2022★ 2 cited

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…

cs.CV2022

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…

cs.CV2022

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…

cs.CV2022

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…