425 citations · 956 across the 28 of their papers we have counts for
5 papers · 1 filter
A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancement
Quan Huu Cap, Atsushi Fukuda, Hitoshi Iyatomi
Medical images are extremely valuable for supporting medical diagnoses. However, in practice, low-quality (LQ) medical images, such as images that are hazy/blurry, have uneven illu…
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval
Kei Nishimaki, Kumpei Ikuta, Yuto Onga +2
Content-based image retrieval (CBIR) systems are an emerging technology that supports reading and interpreting medical images. Since 3D brain MR images are high dimensional, dimens…
MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis
Quan Huu Cap, Hitoshi Iyatomi, Atsushi Fukuda
Medical images have been indispensable and useful tools for supporting medical experts in making diagnostic decisions. However, taken medical images especially throat and endoscopy…
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning
Yuto Onga, Shingo Fujiyama, Hayato Arai +3
Increasing numbers of MRI brain scans, improvements in image resolution, and advancements in MRI acquisition technology are causing significant increases in the demand for and burd…
Super-Resolution for Practical Automated Plant Disease Diagnosis System
Quan Huu Cap, Hiroki Tani, Hiroyuki Uga +2
Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of sy…