Predicting OCT biological marker localization from weak annotations.

Recent developments in deep learning have shown success in accurately predicting the location of biological markers in Optical Coherence Tomography (OCT) volumes of patients with Age-Related Macular Degeneration (AMD) and Diabetic Retinopathy (DR). We propose a method that automatically locates biological markers to the Early Treatment Diabetic Retinopathy Study (ETDRS) rings, only requiring B-scan-level presence annotations. We trained a neural network using 22,723 OCT B-Scans of 460 eyes (433 patients) with AMD and DR, annotated with slice-level labels for Intraretinal Fluid (IRF) and Subretinal Fluid (SRF). The neural network outputs were mapped into the corresponding ETDRS rings. We incorporated the class annotations and domain knowledge into a loss function to constrain the output with biologically plausible solutions. The method was tested on a set of OCT volumes with 322 eyes (189 patients) with Diabetic Macular Edema, with slice-level SRF and IRF presence annotations for the ETDRS rings. Our method accurately predicted the presence of IRF and SRF in each ETDRS ring, outperforming previous baselines even in the most challenging scenarios. Our model was also successfully applied to en-face marker segmentation and showed consistency within C-scans, despite not incorporating volume information in the training process. We achieved a correlation coefficient of 0.946 for the prediction of the IRF area

Explainable AI for retinal OCT diagnosis

Artificial intelligence methods such as deep learning are leading to great progress in complex tasks that are usually associated with human intelligence and experience. Deep learning models have matched if not bettered human performance for medical diagnosis tasks including retinal diagnosis. Given a sufficient amount of data and computational resources, these models can perform classification and segmentation as well as related tasks such as image quality improvement. The adoption of these systems in actual healthcare centers has been limited due to the lack of reasoning behind their decisions. This black box nature along with upcoming regulations for transparency and privacy exacerbates the ethico-legal challenges faced by deep learning systems. The attribution methods are a way to explain the decisions of a deep learning model by generating a heatmap of the features which have the most contribution to the model's decision. These are generally compared in quantitative terms for standard machine learning datasets. However, the ability of these methods to generalize to specific data distributions such as retinal OCT has not been thoroughly evaluated. In this thesis, multiple attribution methods to explain the decisions of deep learning models for retinal diagnosis are compared. It is evaluated if the methods considered the best for explainability outperform the methods with a relatively simpler theoretical background. A review of current deep learning models for retinal diagnosis and the state-of-the-art explainability methods for medical diagnosis is provided. A commonly used deep learning model is trained on a large public dataset of OCT images and the attributions are generated using various methods. A quantitative and qualitative comparison of these approaches is done using several performance metrics and a large panel of experienced retina specialists. The initial quantitative metrics include the runtime of the method, RMSE, and Spearman's rank correlation for a single instance of the model. Later, two stronger metrics - robustness and sensitivity are presented. These evaluate the consistency amongst different instances of the same model and the ability to highlight the features with the most effect on the model output respectively. Similarly, the initial qualitative analysis involves the comparison between the heatmaps and a clinician's markings in terms of cosine similarity. Next, a panel of 14 clinicians rated the heatmaps of each method. Their subjective feedback, reasons for preference, and general feedback about using such a system are also documented. It is concluded that the explainability methods can make the decision process of deep learning models more transparent and the choice of the method should account for the preference of the domain experts. There is a high degree of acceptance from the clinicians surveyed for using such systems. The future directions regarding system improvements and enhancements are also discussed

Fused Detection of Retinal Biomarkers in OCT Volumes

Optical Coherence Tomography (OCT) is the primary imaging modality for detecting pathological biomarkers associated to retinal diseases such as Age-Related Macular Degeneration. In practice, clinical diagnosis and treatment strategies are closely linked to biomarkers visible in OCT volumes and the ability to identify these plays an important role in the development of ophthalmic pharmaceutical products. In this context, we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume. We do so by adding a bidirectional LSTM to fuse the outputs of a Convolutional Neural Network that predicts individual biomarkers. We thus avoid the need to use pixel-wise annotations to train our method and instead provide fine-grained biomarker information regardless. On a dataset of 416 volumes, we show that our approach imposes coherence between biomarker predictions across volume slices and our predictions are superior to several existing approaches