Though deep learning has shown successful performance in classifying the
label and severity stage of certain disease, most of them give few evidence on
how to make prediction. Here, we propose to exploit the interpretability of
deep learning application in medical diagnosis. Inspired by Koch's Postulates,
a well-known strategy in medical research to identify the property of pathogen,
we define a pathological descriptor that can be extracted from the activated
neurons of a diabetic retinopathy detector. To visualize the symptom and
feature encoded in this descriptor, we propose a GAN based method to synthesize
pathological retinal image given the descriptor and a binary vessel
segmentation. Besides, with this descriptor, we can arbitrarily manipulate the
position and quantity of lesions. As verified by a panel of 5 licensed
ophthalmologists, our synthesized images carry the symptoms that are directly
related to diabetic retinopathy diagnosis. The panel survey also shows that our
generated images is both qualitatively and quantitatively superior to existing
methods.

本研究提出一种基于深度学习的医学诊断方法，使用病理描述符从神经元中提取特征，通过生成对抗网络合成具有糖尿病性视网膜病变特征的病理性视网膜图像，并经过专业医生验证证明其优于现有方法。