  In the last decades, large datasets of fundus photographs have been collected
in diabetic retinopathy (DR) screening networks. Through deep learning, these
datasets were used to train automatic detectors for DR and a few other frequent
pathologies, with the goal to automate screening. One challenge limits the
adoption of such systems so far: automatic detectors ignore rare conditions
that ophthalmologists currently detect, such as papilledema or anterior
ischemic optic neuropathy. The reason is that standard deep learning requires
too many examples of these conditions. However, this limitation can be
addressed with few-shot learning, a machine learning paradigm where a
classifier has to generalize to a new category not seen in training, given only
a few examples of this category. This paper presents a new few-shot learning
framework that extends convolutional neural networks (CNNs), trained for
frequent conditions, with an unsupervised probabilistic model for rare
condition detection. It is based on the observation that CNNs often perceive
photographs containing the same anomalies as similar, even though these CNNs
were trained to detect unrelated conditions. This observation was based on the
t-SNE visualization tool, which we decided to incorporate in our probabilistic
model. Experiments on a dataset of 164,660 screening examinations from the
OPHDIAT screening network show that 37 conditions, out of 41, can be detected
with an area under the ROC curve (AUC) greater than 0.8 (average AUC: 0.938).
In particular, this framework significantly outperforms other frameworks for
detecting rare conditions, including multitask learning, transfer learning and
Siamese networks, another few-shot learning solution. We expect these richer
predictions to trigger the adoption of automated eye pathology screening, which
will revolutionize clinical practice in ophthalmology.
