Physics, Instrumentation & Data Sciences -> Data Sciences 1213

1213 - A semi-automated method to separate tumor from physiological uptakes on FDG PET-CT for efficient generation of training data targeting deep learning (ID 534)

Session Name
Data Sciences Posters
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Presentation Topic
Physics, Instrumentation & Data Sciences -> Data Sciences
Presentation Number
1213

Previous Eposter
1212 - Contribution of class activation map on WB PET deep features for primary tumour classification (ID 533)

Next Eposters
1215 - On the use of routine clinical MR images for synthetic CT generation in the head using a deep learning approach (ID 536)
1216 - An AI system using hybrid CNN and Radiomics model to improve PET image quality (ID 537)
1218 - Automated construction of patient-specific computational models using convolutional networks for conceptus dose estimation in pregnant patients (ID 539)
1220 - Usefulness of 18FDG-PET/CT in distinguishing HNF1A-mutated from inflammatory types of hepatocellular adenomas. (ID 540)
1227 - 4FMFES-PET in ER+ lobular breast cancer: revealing what FDG-PET could not (ID 746)
1231 - 99mTc nanocolloid indicyanine green: An hybrid tracer for breast sentinel node procedures (ID 748)
1232 - Relationship between tumor markers, Ca 15-3 and Ca 125, levels and Sodium-18 Fluoride Positron Emission Tomography Scan in breast cancer bone metastasis (ID 747)
1234 - Correlation between 18F-FDG PET/CT standardized uptake value with prognostic factors and gene expression profiles in breast cancer: Single center study from Azerbaijan (ID 752)
1238 - The importance of 18F-FDG PET/CT in staging male breast carcinoma (ID 754)
1240 - High Resolution Breast PET as an invaluable tool to diagnose new breast cancer lesions in patients with dense breast (ID 750)