Shahriar Faghani

According to our database, Shahriar Faghani authored at least 16 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction.

Abdussalam Elhanashi, Sergio Saponara, Qinghe Zheng, Nawal Almutairi, Yashbir Singh, Shiba Kuanar, Farzana Z. Ali, Orhan Unal, Shahriar Faghani

J. Imaging, 2025

Deep Conformal Supervision: Leveraging Intermediate Features for Robust Uncertainty Quantification.

Amir M. Vahdani, Shahriar Faghani

J. Imaging Inform. Medicine, 2025

Correction: Checklist for Reproducibility of Deep Learning in Medical Imaging.

Mana Moassefi, Yashbir Singh, Gian Marco Conte, Bardia Khosravi, Pouria Rouzrokh, Sanaz Vahdati, Nabile M. Safdar, Linda Moy, Felipe Kitamura, Amilcare Gentili, Paras Lakhani, Nina Kottler, Safwan S. Halabi, Joseph H. Yacoub, Yuankai Hou, Khaled Younis, Bradley J. Erickson, Elizabeth A. Krupinski, Shahriar Faghani

J. Imaging Inform. Medicine, 2025

2024

A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.

Sanaz Vahdati, Bardia Khosravi, Elham Mahmoudi, Kuan Zhang, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Aylin Tahmasebi, Katherine P. Andriole, Peter Chang, Keyvan Farahani, Mona G. Flores, Les R. Folio, Sina Houshmand, Maryellen L. Giger, Judy W. Gichoya, Bradley J. Erickson

J. Imaging Inform. Medicine, 2024

Checklist for Reproducibility of Deep Learning in Medical Imaging.

Mana Moassefi, Yashbir Singh, Gian Marco Conte, Bardia Khosravi, Pouria Rouzrokh, Sanaz Vahdati, Nabile M. Safdar, Linda Moy, Felipe Kitamura, Amilcare Gentili, Paras Lakhani, Nina Kottler, Safwan S. Halabi, Joseph H. Yacoub, Yuankai Hou, Khaled Younis, Bradley J. Erickson, Elizabeth A. Krupinski, Shahriar Faghani

J. Imaging Inform. Medicine, 2024

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation.

Dominic LaBella, Katherine Schumacher, Michael Mix, Kevin Leu, Shan McBurney-Lin, Pierre Nedelec, Javier E. Villanueva-Meyer, Jonathan Shapey, Tom Vercauteren, Kazumi Chia, Omar Al-Salihi, Justin Leu, Lia Halasz, Yury Velichko, Chunhao Wang, John Kirkpatrick, Scott Floyd, Zachary J. Reitman, Trey Mullikin, Ulas Bagci, Sean Sachdev, Jona A. Hattangadi-Gluth, Tyler M. Seibert, Nikdokht Farid, Connor Puett, Matthew W. Pease, Kevin Shiue, Syed Muhammad Anwar, Shahriar Faghani, Muhammad Ammar Haider, Pranav I. Warman, Jake Albrecht, András Jakab, Mana Moassefi, Verena Chung, Alejandro Aristizabal, Alexandros Karargyris, Hasan Kassem, Sarthak Pati, Micah J. Sheller, Christina Huang, Aaron Coley, Siddharth Ghanta, Alex Schneider, Conrad Sharp, Rachit Saluja, Florian Kofler, Philipp Lohmann, Philipp Vollmuth, Louis Gagnon, Maruf Adewole, Hongwei Bran Li, Anahita Fathi Kazerooni, Nourel Hoda Tahon, Udunna Anazodo, Ahmed W. Moawad, Bjoern H. Menze, Marius George Linguraru, Mariam Aboian, Benedikt Wiestler, Ujjwal Baid, Gian Marco Conte, Andreas M. Rauschecker, Ayman Nada, Aly H. Abayazeed, Raymond Y. Huang, Maria Correia de Verdier, Jeffrey D. Rudie, Spyridon Bakas, Evan Calabrese

CoRR, 2024

The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI.

Maria Correia de Verdier, Rachit Saluja, Louis Gagnon, Dominic LaBella, Ujjwal Baid, Nourel Hoda Tahon, Martha Foltyn-Dumitru, Jikai Zhang, Maram Alafif, Saif Baig, Ken Chang, Gennaro D'Anna, Lisa Deptula, Diviya Gupta, Muhammad Ammar Haider, Ali Hussain, Michael Iv, Marinos Kontzialis, Paul Manning, Farzan Moodi, Teresa Nunes, Aaron Simon, Nico Sollmann, David Vu, Maruf Adewole, Jake Albrecht, Udunna Anazodo, Rongrong Chai, Verena Chung, Shahriar Faghani, Keyvan Farahani, Anahita Fathi Kazerooni, Juan Eugenio Iglesias, Florian Kofler, Hongwei Li, Marius George Linguraru, Bjoern H. Menze, Ahmed W. Moawad, Yury Velichko, Benedikt Wiestler, Talissa Altes, Patil Basavasagar, Martin Bendszus, Gianluca Brugnara, Jaeyoung Cho, Yaseen Dhemesh, Brandon K. K. Fields, Filip Garrett, Jaime Gass, Lubomir M. Hadjiiski, Jona A. Hattangadi-Gluth, Christopher Hess, Jessica L. Houk, Edvin Isufi, Lester J. Layfield, George Mastorakos, John Mongan, Pierre Nedelec, Uyen Nguyen, Sebastian Oliva, Matthew W. Pease, Aditya Rastogi, Jason Sinclair, Robert X. Smith, Leo P. Sugrue, Jonathan Thacker, Igor Vidic, Javier E. Villanueva-Meyer, Nathan S. White, Mariam Aboian, Gian Marco Conte, Anders M. Dale, Mert R. Sabuncu, Tyler M. Seibert, Brent Weinberg, Aly H. Abayazeed, Raymond Y. Huang, Sevcan Turk, Andreas M. Rauschecker, Nikdokht Farid, Philipp Vollmuth, Ayman Nada, Spyridon Bakas, Evan Calabrese, Jeffrey D. Rudie

CoRR, 2024

BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions.

Spyridon Bakas, Siddhesh P. Thakur, Shahriar Faghani, Mana Moassefi, Ujjwal Baid, Verena Chung, Sarthak Pati, Shubham Innani, Bhakti Baheti, Jake Albrecht, Alexandros Karargyris, Hasan Kassem, MacLean P. Nasrallah, Jared T. Ahrendsen, Valeria Barresi, Maria A. Gubbiotti, Giselle Y. López, Calixto-Hope G. Lucas, Michael L. Miller, Lee A. D. Cooper, Jason T. Huse, William R. Bell

CoRR, 2024

RadRotator: 3D Rotation of Radiographs with Diffusion Models.

Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani, Kellen L. Mulford, Michael J. Taunton, Bradley J. Erickson, Cody C. Wyles

CoRR, 2024

CONFLARE: CONFormal LArge language model REtrieval.

Pouria Rouzrokh, Shahriar Faghani, Cooper U. Gamble, Moein Shariatnia, Bradley J. Erickson

CoRR, 2024

Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection.

Cooper U. Gamble, Shahriar Faghani, Bradley J. Erickson

CoRR, 2024

2023

Few-shot biomedical image segmentation using diffusion models: Beyond image generation.

Bardia Khosravi, Pouria Rouzrokh, John P. Mickley, Shahriar Faghani, Kellen L. Mulford, Linjun Yang, A. Noelle Larson, Benjamin M. Howe, Bradley J. Erickson, Michael J. Taunton, Cody C. Wyles

Comput. Methods Programs Biomed., December, 2023

A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.

Shahriar Faghani, Bardia Khosravi, Mana Moassefi, Gian Marco Conte, Bradley J. Erickson

J. Digit. Imaging, June, 2023

Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review.

Mana Moassefi, Pouria Rouzrokh, Gian Marco Conte, Sanaz Vahdati, Tianyuan Fu, Aylin Tahmasebi, Mira Younis, Keyvan Farahani, Amilcare Gentili, Timothy Kline, Felipe C. Kitamura, Yuankai Huo, Shiba Kuanar, Khaled Younis, Bradley J. Erickson, Shahriar Faghani

J. Imaging Inform. Medicine, 2023

2022

Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report.

Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani, Mana Moassefi, Sanaz Vahdati, Bradley J. Erickson

CoRR, 2022

2021

MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks.

Benjamin B. Yan, Yujia Wei, Jaidip Manikrao M. Jagtap, Mana Moassefi, Diana V. Vera Garcia, Yashbir Singh, Sanaz Vahdati, Shahriar Faghani, Bradley J. Erickson, Gian Marco Conte

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021