# Uncertainty-Aware Training for Ophthalmic Segmentation Using MedSAM

## TL;DR

This paper introduces a method called Uncertainty-Aware Training (UAT) that improves deep learning models for eye-related image segmentation by focusing on uncertain areas during training.

## Contribution

UAT introduces uncertainty maps during training to guide model learning, improving performance on ophthalmic segmentation tasks.

## Key findings

- UAT improved model performance on three ophthalmic segmentation tasks: GA, OC, and FAZ.
- Entropy-weighing maps consistently enhanced performance across all datasets.
- Conformal prediction with Least Ambiguous Set-Valued Classifier improved GA and OC segmentation.

## Abstract

Uncertainty quantification (UQ) has been applied to deep learning (DL) models to enhance not only their interpretability, but to guide model learning. This work presents Uncertainty-Aware Training (UAT), which generates uncertainty maps at train time and augments the loss function with them to allow the models to concentrate on areas of high uncertainty during learning. By highlighting uncertain pixels as requiring more scrutiny, model performance is enhanced on three ophthalmic segmentation tasks.

We applied UAT to three ophthalmic segmentation, or pixel-level classification, tasks: geographic atrophy (GA), optic cup (OC), and foveal avascular zone (FAZ). UAT weighs the loss function binary cross-entropy according to uncertainty, forcing the model to focus on ambiguous areas. We experimented with output-based UQ methods, such as entropy, and post -hoc techniques like conformal prediction. UAT was evaluated on a state-of-the-art foundational model fine-tuned for these specific segmentation tasks.

The addition of an entropy-weighing map at loss calculation time consistently improved model performance across all three datasets, with conformal prediction using the Least Ambiguous Set-Valued Classifier version also achieving higher overall performance for GA and OC segmentation. The easy-to-integrate implementation of UAT proved feasible without significant training structure modifications.

UAT enhances DL model performance and interpretability by focusing on uncertainty in addition to error, improving segmentation accuracy.

UAT uses interpretable uncertainty maps to enhance DL segmentation performance. Its lightweight integration makes it practical for adoption and leads to improved model performance on ophthalmic segmentation tasks.