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ORCIDShadab Ahamed
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2020 – today
- 2024
[i7]Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise, Claire Gowdy, Patrick Martineau, Don Wilson, Carlos F. Uribe, Arman Rahmim, Fereshteh Yousefirizi:
A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma. CoRR abs/2403.07092 (2024)- [i6]Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O, Carlos F. Uribe, Rahul Dodhia, Juan Lavista Ferres, Arman Rahmim:
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset. CoRR abs/2403.07105 (2024) - [i5]Shadab Ahamed, Yixi Xu, Arman Rahmim:
IgCONDA-PET: Implicitly-Guided Counterfactual Diffusion for Detecting Anomalies in PET Images. CoRR abs/2405.00239 (2024) - [i4]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:
Thyroidiomics: An Automated Pipeline for Segmentation and Classification of Thyroid Pathologies from Scintigraphy Images. CoRR abs/2407.10336 (2024) - [i3]Shadab Ahamed:
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. CoRR abs/2409.10151 (2024) - 2023
- [c5]Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo Hyun O, Carlos F. Uribe, Rahul Dodhia, Juan Lavista Ferres, Arman Rahmim:
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset. Medical Imaging: Image Processing 2023 - [c4]Amirhosein Toosi, Sara Harsini
, Shadab Ahamed, Fereshteh Yousefirizi, François Bénard, Carlos F. Uribe, Arman Rahmim:
State-of-the-art object detection algorithms for small lesion detection in PSMA PET: use of rotational maximum intensity projection (MIP) images. Medical Imaging: Image Processing 2023 - [i2]Shadab Ahamed, Arman Rahmim:
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images. CoRR abs/2309.13553 (2023) - [i1]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:
Comprehensive Evaluation and Insights into the Use of Deep Neural Networks to Detect and Quantify Lymphoma Lesions in PET/CT Images. CoRR abs/2311.09614 (2023) - 2022
- [c3]Shadab Ahamed, Luke Polson, Arman Rahmim:
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. HECKTOR@MICCAI 2022: 94-106 - [c2]Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise, Claire Gowdy, Patrick Martineau, Don Wilson, Carlos F. Uribe, Arman Rahmim, Fereshteh Yousefirizi:
A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma. Medical Imaging: Image Processing 2022 - [c1]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:
Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images. Medical Imaging: Image Processing 2022
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