9 citations · 16 across the 14 of their papers we have counts for
15 papers
KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level
Ruining Deng, Tianyuan Yao, Yucheng Tang +44
Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney biopsy remains the gold standard f…
Color-Quality Invariance for Robust Medical Image Segmentation
Ravi Shah, Atsushi Fukuda, Quan Huu Cap
Single-source domain generalization (SDG) in medical image segmentation remains a significant challenge, particularly for images with varying color distributions and qualities. Pre…
From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis
Kristofer Grover Roos, Atsushi Fukuda, Quan Huu Cap
While functional magnetic resonance imaging (fMRI) offers valuable insights into brain activity, it is limited by high operational costs and significant infrastructural demands. In…
A General Pipeline for Glomerulus Whole-Slide Image Segmentation
Quan Huu Cap
Whole-slide images (WSI) glomerulus segmentation is essential for accurately diagnosing kidney diseases. In this work, we propose a general and practical pipeline for glomerulus se…
High-Quality Medical Image Generation from Free-hand Sketch
Quan Huu Cap, Atsushi Fukuda
Generating medical images from human-drawn free-hand sketches holds promise for various important medical imaging applications. Due to the extreme difficulty in collecting free-han…
Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy
Quan Huu Cap, Atsushi Fukuda, Satoshi Kagiwada +3
With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based sys…
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…
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box Training
Hideaki Okamoto, Quan Huu Cap, Takakiyo Nomura +3
Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be dia…
Bulk Production Augmentation Towards Explainable Melanoma Diagnosis
Kasumi Obi, Quan Huu Cap, Noriko Umegaki-Arao +2
Although highly accurate automated diagnostic techniques for melanoma have been reported, the realization of a system capable of providing diagnostic evidence based on medical indi…
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…
LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis
Quan Huu Cap, Hiroki Tani, Hiroyuki Uga +2
The collection of high-resolution training data is crucial in building robust plant disease diagnosis systems, since such data have a significant impact on diagnostic performance.…
LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis
Quan Huu Cap, Hiroyuki Uga, Satoshi Kagiwada +1
Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from o…
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…
AOP: An Anti-overfitting Pretreatment for Practical Image-based Plant Diagnosis
Takumi Saikawa, Quan Huu Cap, Satoshi Kagiwada +2
In image-based plant diagnosis, clues related to diagnosis are often unclear, and the other factors such as image backgrounds often have a significant impact on the final decision.…
A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images
Katsumasa Suwa, Quan Huu Cap, Ryunosuke Kotani +3
Practical automated detection and diagnosis of plant disease from wide-angle images (i.e. in-field images containing multiple leaves using a fixed-position camera) is a very import…