Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task.

Hayato ItohMasahiro OdaYuichi MoriMasashi MisawaShin-Ei KudoKenichiro ImaiSayo ItoKinichi HottaHirotsugu TakabatakeMasaki MoriHiroshi NatoriKensaku Mori

Published in: International journal of computer assisted radiology and surgery (2021)

We developed an accurate depth-estimation method with a new type of unsupervised domain translation with the auxiliary task. This method is useful for analysis of colonoscopic images and for the development of a CAD system since it can extract accurate 3D information.

Keyphrases

optical coherence tomography
machine learning
high resolution
coronary artery disease
deep learning
oxidative stress
healthcare
mass spectrometry
anti inflammatory