TY - GEN
T1 - DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
AU - Huang, Jia-Hong
AU - Yang, C.-H. Huck
AU - Liu, Fangyu
AU - Tian, Meng
AU - Liu, Yi-Chieh
AU - Wu, Ting-Wei
AU - Lin, I-Hung
AU - Wang, Kang
AU - Morikawa, Hiromasa
AU - Chang, Hernghua
AU - Tegner, Jesper
AU - Worring, Marcel
N1 - KAUST Repository Item: Exported on 2021-06-17
Acknowledgements: This work is supported by competitive research funding from King Abdullah University of Science and Technology (KAUST) and University of Amsterdam.
PY - 2021
Y1 - 2021
N2 - In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologists to qualitatively show the proposed AI-based method is effective. With our experimental results, we show that the proposed method is quantitatively and qualitatively effective. Our method is capable of creating meaningful retinal image descriptions and visual explanations that are clinically relevant.
AB - In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologists to qualitatively show the proposed AI-based method is effective. With our experimental results, we show that the proposed method is quantitatively and qualitatively effective. Our method is capable of creating meaningful retinal image descriptions and visual explanations that are clinically relevant.
UR - http://hdl.handle.net/10754/665874
UR - https://openaccess.thecvf.com/content/WACV2021/html/Huang_DeepOpht_Medical_Report_Generation_for_Retinal_Images_via_Deep_Models_WACV_2021_paper.html
U2 - 10.1109/WACV48630.2021.00249
DO - 10.1109/WACV48630.2021.00249
M3 - Conference contribution
SN - 978-1-6654-4640-2
BT - 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
PB - IEEE
ER -