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Found 5 papers, 3 papers with code
IgCONDA-PET: Implicitly-Guided Counterfactual Diffusion for Detecting Anomalies in PET Images
Minimizing the need for pixel-level annotated data for training PET anomaly segmentation networks is crucial, particularly due to time and cost constraints related to expert annotations.
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
Various instances of the network were trained on 2D axial datasets created in different ways: (i) slice-level split and (ii) patient-level split; inputs of different types were used: (i) only PET slices and (ii) concatenated PET and CT slices; and different training strategies were employed: (i) center-aware (CAW) and (ii) center-agnostic (CAG).
A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma
Manual segmentation of tumors in whole-body PET images is time-consuming, labor-intensive and operator-dependent.
Comprehensive Evaluation and Insights into the Use of Deep Neural Networks to Detect and Quantify Lymphoma Lesions in PET/CT Images
This study performs comprehensive evaluation of four neural network architectures (UNet, SegResNet, DynUNet, and SwinUNETR) for lymphoma lesion segmentation from PET/CT images.
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images
These lesions can be found in various parts of the body, often close to healthy organs that also show significant uptake.
