DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
This work addresses the need to improve diagnosis efficiency and accuracy for ophthalmologists in retinal disease treatment, but it appears incremental as it builds on existing deep learning approaches without claiming major breakthroughs.
The authors tackled the problem of generating medical reports for retinal images by proposing an AI method that combines deep neural networks for disease identification and description generation with visual explanation modules, and they demonstrated its effectiveness quantitatively and qualitatively using a new large-scale dataset.
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.