Machine learning, specifically artificial deep neural network, have been used extensively in the last decade to tackle problems in computer vision. Medical imaging is one field of application whose large potential has slowly started to be understood and exploited. Within medical imaging Optical Coherence Tomography (OCT) is an imaging technique which is based on low-coherence interferometry and employed in ophthalmology where it yields large quantities of detailed images from within the eye, in particular, from the retina.
The field of ophthalmology investigates various eye anomalies, e.g. eye diseases, which are distinguishable on OCT images. Thus, recently, numerous supervised machine learning approaches have trained artificial neural networks for classification and semantic image segmentation tasks. Based on this the explainability of these models is investigated, e.g. by Layer-wise Relevance Propagation (LRP)1.
On the other hand, many eye anomalies are rare and training data is little. For example in the case of subretinal/intraretinal liquids2,3, hyperreflective foci4, and subretinal hyperreflective material5. In such cases, anomalies can be detected in an unsupervised learning setting through anomaly detection by employing e.g. autoencoder algorithms (possibly in combination with Generative Adversarial Networks, GANs)6,7. However, the study of explainability in these cases has been limited so far. But questions like “which area of the input image lead to the classification of an image as outlier/anomaly” are very important for medical professionals.