# DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation Source: [DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:42:59.203Z Authors: Jia-Hong Huang, Chao-Han Huck Yang, Fangyu Liu, Meng Tian, Yi-Chieh Liu, Ting-Wei Wu, I-Hung Lin, Kang Wang, Hiromasa Morikawa, Hernghua Chang, Jesper Tegner, Marcel Worring Published: 2020-11-01T17:28:12.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Medical Report Generation on DeepEyeNet](): Medical Report Generation; DeepEyeNet; BLEU-1: 0.184. ## Original sources and implementation [Original paper]() [PDF]()