Gradient based Severity Labeling for Biomarker Classification in OCT

TL;DR

A method that generates disease severity labels for unlabeled OCT scans on the basis of gradient responses from an anomaly detection algorithm is introduced to train a supervised contrastive learning setup to improve biomarker classification accuracy by as much as 6% above self-supervised baselines for key indicators of Diabetic Retinopathy.

Abstract

In this paper, we propose a novel selection strategy for contrastive learning for medical images. On natural images, contrastive learning uses augmentations to select positive and negative pairs for the contrastive loss. However, in the medical domain, arbitrary augmentations have the potential to distort small localized regions that contain the biomarkers we are interested in detecting. A more intuitive approach is to select samples with similar disease severity characteristics, since these samples are more likely to have similar structures related to the progression of a disease. To enable this, we introduce a method that generates disease severity labels for unlabeled OCT scans on the basis of gradient responses from an anomaly detection algorithm. These labels are used to train a supervised contrastive learning setup to improve biomarker classification accuracy by as much as 6% above self-supervised baselines for key indicators of Diabetic Retinopathy.

Figures (5)

• Figure 1: OCT Scans of biomarkers from the Prime + TREX DME datasets. The biomarkers are A) Intraretinal Hyperreflective Foci (IRHRF), B) Intraretinal Fluid (IRF) and Diabetic Macular Edema (DME) C) Partially Attached Vitreous Face (PAVF), and D) Fully Attached Vitreous Face (FAVF). The white arrows point out examples of where these biomarkers are found in the images. A discussion of biomarkers in OCT can be found at markan2020novel.
• Figure 2: From a healthy manifold learned from a trained auto-encoder, we can compute distance to the manifold of more severely diseased cases via a Severity Score (SS). Severity Score is calculated via some model response and increases as a sample is more anomalous compared to the learned healthy manifold.
• Figure 3: Overview of severity labeling methodology.
• Figure 4: Overview of supervised contrastive learning and linear fine-tuning steps. 1) Supervised Contrastive Loss on generated severity labels from previously unlabeled data. 2) Attach linear layer and train on labeled biomarker data.
• Figure 5: Visual representation of OCT scans with high and low severity scores.