Shadab Ahamed

According to our database, Shadab Ahamed authored at least 13 papers between 2022 and 2025.

Collaborative distances:

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

2025

PyTomography: A python library for medical image reconstruction.

Lucas A. Polson, Roberto Fedrigo, Chenguang Li, Maziar Sabouri, Obed Dzikunu, Shadab Ahamed, Nicolas A. Karakatsanis, Sara Kurkowska, Peyman Sheikhzadeh, Pedro L. Esquinas, Arman Rahmim, Carlos F. Uribe

SoftwareX, 2025

2024

DAWN-SI: Data-Aware and Noise-Informed Stochastic Interpolation for Solving Inverse Problems.

Shadab Ahamed, Eldad Haber

CoRR, 2024

AutoPET Challenge III: Testing the Robustness of Generalized Dice Focal Loss trained 3D Residual UNet for FDG and PSMA Lesion Segmentation from Whole-Body PET/CT Images.

Shadab Ahamed

CoRR, 2024

IgCONDA-PET: Implicitly-Guided Counterfactual Diffusion for Detecting Anomalies in PET Images.

Shadab Ahamed, Yixi Xu, Arman Rahmim

CoRR, 2024

A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset.

Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O, Carlos F. Uribe, Rahul Dodhia, Juan Lavista Ferres, Arman Rahmim

CoRR, 2024

Thyroidiomics: An Automated Pipeline for Segmentation and Classification of Thyroid Pathologies from Scintigraphy Images.

Maziar Sabouri, Shadab Ahamed, Azin Asadzadeh, Atlas Haddadi Avval, Soroush Bagheri, Mohsen Arabi, Seyed Rasoul Zakavi, Emran Askari, Ali Rasouli, Atena Aghaee, Mohaddese Sehati, Fereshteh Yousefirizi, Carlos F. Uribe, Ghasem Hajianfar, Habib Zaidi, Arman Rahmim

Proceedings of the 12th European Workshop on Visual Information Processing, 2024

2023

Comprehensive Evaluation and Insights into the Use of Deep Neural Networks to Detect and Quantify Lymphoma Lesions in PET/CT Images.

Shadab Ahamed, Yixi Xu, Claire Gowdy, Joo Hyun O, Ingrid Bloise, Don Wilson, Patrick Martineau, François Bénard, Fereshteh Yousefirizi, Rahul Dodhia, Juan M. Lavista Ferres, William B. Weeks, Carlos F. Uribe, Arman Rahmim

CoRR, 2023

Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images.

Shadab Ahamed, Arman Rahmim

CoRR, 2023

State-of-the-art object detection algorithms for small lesion detection in PSMA PET: use of rotational maximum intensity projection (MIP) images.

Amirhosein Toosi, Sara Harsini, Shadab Ahamed, Fereshteh Yousefirizi, François Bénard, Carlos F. Uribe, Arman Rahmim

Proceedings of the Medical Imaging 2023: Image Processing, 2023

A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset.

Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo Hyun O, Carlos F. Uribe, Rahul Dodhia, Juan Lavista Ferres, Arman Rahmim

Proceedings of the Medical Imaging 2023: Image Processing, 2023

2022

Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images.

Fereshteh Yousefirizi, Natalia Dubljevic, Shadab Ahamed, Ingrid Bloise, Claire Gowdy, Joo Hyun O, Youssef Farag, Rodrigue de Schaetzen, Patrick Martineau, Don Wilson, Carlos F. Uribe, Arman Rahmim

Proceedings of the Medical Imaging 2022: Image Processing, 2022

A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma.

Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise, Claire Gowdy, Patrick Martineau, Don Wilson, Carlos F. Uribe, Arman Rahmim, Fereshteh Yousefirizi

Proceedings of the Medical Imaging 2022: Image Processing, 2022

A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors and Lymph Nodes from Head and Neck Cancer PET/CT Images.

Shadab Ahamed, Luke Polson, Arman Rahmim

Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2022