Khashayar Namdar

According to our database, Khashayar Namdar authored at least 16 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

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, Uri Tabori, Birgit B. Ertl-Wagner, Farzad Khalvati

CoRR, 2024

2023

Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification.

Sajith Rajapaksa, Khashayar Namdar, Farzad Khalvati

Proceedings of the Medical Image Learning with Limited and Noisy Data, 2023

Multimodal Learning for Improving Performance and Explainability of Chest X-Ray Classification.

Sara Ketabi, Pranav Agnihotri, Hamed Zakeri, Khashayar Namdar, Farzad Khalvati

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops, 2023

2022

A Comprehensive Study of Radiomics-based Machine Learning for Fibrosis Detection.

Jay J. Yoo, Khashayar Namdar, Chris McIntosh, Farzad Khalvati, Patrik Rogalla

CoRR, 2022

Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation.

Chaojun Chen, Khashayar Namdar, Yujie Wu, Shahob Hosseinpour, Manohar Shroff, Andrea S. Doria, Farzad Khalvati

CoRR, 2022

A novel GAN-based paradigm for weakly supervised brain tumor segmentation of MR images.

Jay J. Yoo, Khashayar Namdar, Matthias W. Wagner, Liana Nobre, Uri Tabori, Cynthia Hawkins, Birgit B. Ertl-Wagner, Farzad Khalvati

CoRR, 2022

Tumor-location-guided CNNs for Pediatric Low-grade Glioma Molecular Biomarker Classification Using MRI.

Khashayar Namdar, Matthias W. Wagner, Kareem Kudus, Cynthia Hawkins, Uri Tabori, Birgit Ertl-Wagner, Farzad Khalvati

CoRR, 2022

Superpixel Generation and Clustering for Weakly Supervised Brain Tumor Segmentation in MR Images.

Jay J. Yoo, Khashayar Namdar, Farzad Khalvati

CoRR, 2022

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

CoRR, 2022

Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology.

Pranav Agnihotri, Sara Ketabi, Khashayar Namdar, Farzad Khalvati

CoRR, 2022

Improving Disease Classification Performance and Explainability of Deep Learning Models in Radiology with Heatmap Generators.

Akino Watanabe, Sara Ketabi, Khashayar Namdar, Farzad Khalvati

CoRR, 2022

2021

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, Masoom A. Haider, Farzad Khalvati

J. Digit. Imaging, 2021

A Modified AUC for Training Convolutional Neural Networks: Taking Confidence Into Account.

Khashayar Namdar, Masoom A. Haider, Farzad Khalvati

Frontiers Artif. Intell., 2021

A Transfer Learning-Based Active Learning Framework for Brain Tumor Classification.

Ruqian Hao, Khashayar Namdar, Lin Liu, Farzad Khalvati

Frontiers Artif. Intell., 2021

2020

A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning.

Partoo Vafaeikia, Khashayar Namdar, Farzad Khalvati

CoRR, 2020

2019

Evolution-based Fine-tuning of CNNs for Prostate Cancer Detection.

Khashayar Namdar, Isha Gujrathi, Masoom A. Haider, Farzad Khalvati

CoRR, 2019