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Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy
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SYSNO ASEP 0538241 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy Author(s) Murray, Sean Ernest (UTIA-B)
Quinn, Anthony (UTIA-B) ORCIDNumber of authors 2 Issue data Praha: ÚTIA AV ČR, 2020 Series Research Report Series number 2388 Publication form Print - P Language eng - English Country CZ - Czech Republic Keywords Bayesian estimation ; thyroid carcinoma ; patient-specific inferences Subject RIV BD - Theory of Information OECD category Applied mathematics R&D Projects GA18-15970S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 Annotation This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid carcinoma. The work seeks to address some limitations of previous approaches [4] which involve generic, non-selective transfer of archival data. It is proposed that improvements in patient-specific inferences may be achieved via transferring external population knowledge selectively. This involves matching the patient to a similar sub-population based on available metadata, generating a Gaussian Mixture Model within the partitioned data, and optimally transferring a data predictive distribution from the sub-population to the specific patient. Additionally, a performance evaluation method is proposed and early-stage results presented. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2021
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