13 citations · 30 across the 17 of their papers we have counts for
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cs.CV2023
Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses
Shadab Ahamed, Yixi Xu, Sara Kurkowska +12
This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing literature. While deep learning has been a…
eess.IV2023★ 3 cited
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images
Shadab Ahamed, Arman Rahmim
Automated segmentation of cancerous lesions in PET/CT images is a vital initial task for quantitative analysis. However, it is often challenging to train deep learning-based segmen…
physics.med-ph2023★ 2 cited
PyTomography: A Python Library for Medical Image Reconstruction
Lucas A. Polson, Roberto Fedrigo, Chenguang Li +8
There is a need for open-source libraries in emission tomography that (i) use modern and popular backend code to encourage community contributions and (ii) offer support for the mu…