Disease Severity Regression with Continuous Data Augmentation

no code implementations • 24 Feb 2023 • Shumpei Takezaki, Kiyohito Tanaka, Seiichi Uchida, Takeaki Kadota

We propose continuous DA as a solution to the two issues.

Data Augmentation Generative Adversarial Network +1

Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data

no code implementations • 5 Aug 2022 • Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Seiichi Uchida

This paper proposes a deep Bayesian active-learning-to-rank, which trains a Bayesian convolutional neural network while automatically selecting appropriate pairs for relative annotation.

Active Learning Learning-To-Rank

Realistic Endoscopic Image Generation Method Using Virtual-to-real Image-domain Translation

no code implementations • 13 Jan 2022 • Masahiro Oda, Kiyohito Tanaka, Hirotsugu TAKABATAKE, Masaki MORI, Hiroshi NATORI, Kensaku MORI

Virtual endoscopic images are generated by using a volume rendering method from a CT volume of a patient.

Image Generation Translation

Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation

no code implementations • 12 Jan 2022 • Masahiro Oda, Hayato Itoh, Kiyohito Tanaka, Hirotsugu TAKABATAKE, Masaki MORI, Hiroshi NATORI, Kensaku MORI

The identification accuracy of the network improved from 69. 2% to 74. 1% by using the estimated depth images.

Depth Estimation Domain Adaptation +1

Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels

no code implementations • 6 Nov 2021 • Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida

Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties.

Classification Disentanglement