A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.

ABSTRACT:

Background

Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images.

Methods

The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentat

SUBMITTER: Liu X 

PROVIDER: S-EPMC10784049 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature