Congress:
ECR25
Poster Number:
C-27014
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
I. Stamouli1, I. Gatos1, P. Katsakiori1, S. Tsantis1, T. Kalathas2, V. Chatzipavlidou2, N. Papathanasiou1, G. Kagadis1; 1Rion/GR, 2Thessaloniki/GR
Disclosures:
Ioanna Stamouli:
Nothing to disclose
Ilias Gatos:
Nothing to disclose
Paraskevi Katsakiori:
Nothing to disclose
Stavros Tsantis:
Nothing to disclose
Theodoros Kalathas:
Nothing to disclose
Vasiliki Chatzipavlidou:
Nothing to disclose
Nikos Papathanasiou:
Nothing to disclose
George Kagadis:
Nothing to disclose
Keywords:
Artificial Intelligence, Molecular imaging, Nuclear medicine, PET-CT, CAD, Molecular imaging, Segmentation, Cancer, Molecular, genomics and proteomics, Multidisciplinary cancer care
The DenseNet201 model demonstrates robust performance in the classification of FP and TP findings in 18F-PSMA PET/CT images of biochemical recurrent patients with PCa. The introduction of data augmentation techniques led to improvements in specificity, accuracy, and overall model performance, as reflected in the higher AUROCC values.