17 citations · 38 across the 5 of their papers we have counts for
5 papers
Disease Severity Regression with Continuous Data Augmentation
Shumpei Takezaki, Kiyohito Tanaka, Seiichi Uchida +1
Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of image samples labeled with severity levels. Conditional gener…
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data
Takeaki Kadota, Hideaki Hayashi, Ryoma Bise +2
Automatic image-based disease severity estimation generally uses discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult due to the images with am…
Realistic Endoscopic Image Generation Method Using Virtual-to-real Image-domain Translation
Masahiro Oda, Kiyohito Tanaka, Hirotsugu Takabatake +3
This paper proposes a realistic image generation method for visualization in endoscopic simulation systems. Endoscopic diagnosis and treatment are performed in many hospitals. To r…
Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation
Masahiro Oda, Hayato Itoh, Kiyohito Tanaka +4
We propose a depth estimation method from a single-shot monocular endoscopic image using Lambertian surface translation by domain adaptation and depth estimation using multi-scale…
Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels
Shota Harada, Ryoma Bise, Hideaki Hayashi +2
Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties. First, endoscopic images with the annotation about UC (…