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With recent advances in supervised machine learning for medical imageanalysis applications, the annotated medical image datasets of various domainsare being shared extensively. Given that the annotation labelling requiresmedical expertise, such labels should be applied to as many learning tasks aspossible. However, the multi-modal nature of each annotated image renders itdifficult to share the annotation label among diverse tasks. In this work, weprovide an inductive transfer learning (ITL) approach to adopt the annotationlabel of the source domain datasets to tasks of the target domain datasetsusing Cycle-GAN based unsupervised domain adaptation (UDA). To evaluate theapplicability of the ITL approach, we adopted the brain tissue annotation labelon the source domain dataset of Magnetic Resonance Imaging (MRI) images to thetask of brain tumor segmentation on the target domain dataset of MRI. Theresults confirm that the segmentation accuracy of brain tumor segmentationimproved significantly. The proposed ITL approach can make significantcontribution to the field of medical image analysis, as we develop afundamental tool to improve and promote various tasks using medical images.

An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation