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

19 citations · 21 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2021★ 2 cited

Symbolic integration by integrating learning models with different strengths and weaknesses

Hazumi Kubota, Yuta Tokuoka, Takahiro G. Yamada +1

Integration is indispensable, not only in mathematics, but also in a wide range of other fields. A deep learning method has recently been developed and shown to be capable of integ…

cs.CV2020★ 19 cited

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

Yuta Tokuoka, Shuji Suzuki, Yohei Sugawara

With recent advances in supervised machine learning for medical image analysis applications, the annotated medical image datasets of various domains are being shared extensively. G…