425 citations · 956 across the 28 of their papers we have counts for
14 papers · 1 filter
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…
Feedback is Needed for Retakes: An Explainable Poor Image Notification Framework for the Visually Impaired
Kazuya Ohata, Shunsuke Kitada, Hitoshi Iyatomi
We propose a simple yet effective image captioning framework that can determine the quality of an image and notify the user of the reasons for any flaws in the image. Our framework…
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI
Hayato Arai, Yuto Onga, Kumpei Ikuta +3
To build a robust and practical content-based image retrieval (CBIR) system that is applicable to a clinical brain MRI database, we propose a new framework -- Disease-oriented imag…
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…
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…