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Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy

This paper 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 addresses some limitations of previous approaches which involved 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 and formally transferring a feature-space-conditioned, probabilistic data predictor from the sub-population to the specific patient. In addition, the transfer times are chosen to complement the patient’s own data. Currently the proposed method yields positive transfer, with stable performance improvements up to 34%. Although this is found to be 9% below the performance of the current state-of-the-art, the proposed method is significant in that it can be applied to other transfer learning applications where inhomogeneous parameter knowledge is available in the source feature space.