no code implementations • 1 Feb 2024 • Quan Huu Cap, Atsushi Fukuda
Generating medical images from human-drawn free-hand sketches holds promise for various important medical imaging applications.
no code implementations • 5 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.
1 code implementation • 4 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.
no code implementations • 3 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.
no code implementations • 28 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.
no code implementations • 12 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.
1 code implementation • 24 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.
no code implementations • 26 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.
no code implementations • 25 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.
no code implementations • 25 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.