  Diabetic retinopathy (DR) is a primary cause of blindness in working-age
people worldwide. About 3 to 4 million people with diabetes become blind
because of DR every year. Diagnosis of DR through color fundus images is a
common approach to mitigate such problem. However, DR diagnosis is a difficult
and time consuming task, which requires experienced clinicians to identify the
presence and significance of many small features on high resolution images.
Convolutional Neural Network (CNN) has proved to be a promising approach for
automatic biomedical image analysis recently. In this work, we investigate
lesion detection on DR fundus images with CNN-based object detection methods.
Lesion detection on fundus images faces two unique challenges. The first one is
that our dataset is not fully labeled, i.e., only a subset of all lesion
instances are marked. Not only will these unlabeled lesion instances not
contribute to the training of the model, but also they will be mistakenly
counted as false negatives, leading the model move to the opposite direction.
The second challenge is that the lesion instances are usually very small,
making them difficult to be found by normal object detectors. To address the
first challenge, we introduce an iterative training algorithm for the
semi-supervised method of pseudo-labeling, in which a considerable number of
unlabeled lesion instances can be discovered to boost the performance of the
lesion detector. For the small size targets problem, we extend both the input
size and the depth of feature pyramid network (FPN) to produce a large CNN
feature map, which can preserve the detail of small lesions and thus enhance
the effectiveness of the lesion detector. The experimental results show that
our proposed methods significantly outperform the baselines.
