4 papers
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 +13
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…
Semi-supervised learning towards automated segmentation of PET images with limited annotations: Application to lymphoma patients
Fereshteh Yousefirizi, Isaac Shiri, Joo Hyun O +9
The time-consuming task of manual segmentation challenges routine systematic quantification of disease burden. Convolutional neural networks (CNNs) hold significant promise to reli…
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 +5
Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a h…
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 +6
Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics an…