Search Results for author: Quan Huu Cap

Found 10 papers, 2 papers with code

High-Quality Medical Image Generation from Free-hand Sketch

no code implementations1 Feb 2024 Quan Huu Cap, Atsushi Fukuda

Generating medical images from human-drawn free-hand sketches holds promise for various important medical imaging applications.

Image Generation Medical Image Generation

Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy

no code implementations5 Sep 2023 Quan Huu Cap, Atsushi Fukuda, Satoshi Kagiwada, Hiroyuki Uga, Nobusuke Iwasaki, Hitoshi Iyatomi

Hard-sample mining (HSM) is a common technique for re-training a model by using the mis-detected boxes as new training samples.

object-detection Object Detection

A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancement

1 code implementation4 Apr 2023 Quan Huu Cap, Atsushi Fukuda, Hitoshi Iyatomi

In this study, we propose a framework for practical unsupervised medical image enhancement that includes (1) a non-reference objective evaluation of structure preservation for medical image enhancement tasks called Laplacian structural similarity index measure (LaSSIM), which is based on SSIM and the Laplacian pyramid, and (2) a novel unsupervised GAN-based method called Laplacian medical image enhancement (LaMEGAN) to support the improvement of both originality and quality from LQ images.

Image Enhancement Medical Image Enhancement +1

Bulk Production Augmentation Towards Explainable Melanoma Diagnosis

no code implementations3 Mar 2021 Kasumi Obi, Quan Huu Cap, Noriko Umegaki-Arao, Masaru Tanaka, Hitoshi Iyatomi

Although highly accurate automated diagnostic techniques for melanoma have been reported, the realization of a system capable of providing diagnostic evidence based on medical indices remains an open issue because of difficulties in obtaining reliable training data.

Data Augmentation Melanoma Diagnosis

MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis

no code implementations28 Nov 2020 Quan Huu Cap, Hitoshi Iyatomi, Atsushi Fukuda

Medical images have been indispensable and useful tools for supporting medical experts in making diagnostic decisions.

Image-to-Image Translation Medical Diagnosis +1

LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis

no code implementations12 Oct 2020 Quan Huu Cap, Hiroki Tani, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi

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.

Super-Resolution

LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis

1 code implementation24 Feb 2020 Quan Huu Cap, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi

LeafGAN generates a wide variety of diseased images via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis.

Data Augmentation Image-to-Image Translation +1

Super-Resolution for Practical Automated Plant Disease Diagnosis System

no code implementations26 Nov 2019 Quan Huu Cap, Hiroki Tani, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi

Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of symptoms are lost.

Super-Resolution

AOP: An Anti-overfitting Pretreatment for Practical Image-based Plant Diagnosis

no code implementations25 Nov 2019 Takumi Saikawa, Quan Huu Cap, Satoshi Kagiwada, Hiroyuki Uga, Hitoshi Iyatomi

As a result, overfitting due to latent similarities in the dataset often occurs, and the diagnostic performance on real unseen data (e, g.

A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images

no code implementations25 Oct 2019 Katsumasa Suwa, Quan Huu Cap, Ryunosuke Kotani, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi

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 important application for large-scale farm management, in view of the need to ensure global food security.

Management Object Recognition

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