  Diabetic retinopathy (DR) is a diabetes complication that affects eyes. DR is
a primary cause of blindness in working-age people and it is estimated that 3
to 4 million people with diabetes are blinded by DR every year worldwide. Early
diagnosis have been considered an effective way to mitigate such problem. The
ultimate goal of our research is to develop novel machine learning techniques
to analyze the DR images generated by the fundus camera for automatically DR
diagnosis. In this paper, we focus on identifying small lesions on DR fundus
images. The results from our analysis, which include the lesion category and
their exact locations in the image, can be used to facilitate the determination
of DR severity (indicated by DR stages). Different from traditional object
detection for natural images, lesion detection for fundus images have unique
challenges. Specifically, the size of a lesion instance is usually very small,
compared with the original resolution of the fundus images, making them
diffcult to be detected. We analyze the lesion-vs-image scale carefully and
propose a large-size feature pyramid network (LFPN) to preserve more image
details for mini lesion instance detection. Our method includes an effective
region proposal strategy to increase the sensitivity. The experimental results
show that our proposed method is superior to the original feature pyramid
network (FPN) method and Faster RCNN.
