论文标题
通过自适应课程和功能分解，学习稳健的眼科疾病联合分级
Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement
论文作者
论文摘要
糖尿病性视网膜病（DR）和糖尿病性黄斑水肿（DME）是全球永久失明的主要原因。在临床实践中设计具有良好概括能力的自动分级系统至关重要。但是，先前的工作是独立的DR或DME等级，而无需考虑它们之间的内部相关性，或者通过共享特征表示共同对它们进行分级，但忽略了由困难的样本和数据偏见引起的潜在概括问题。为了解决这些问题，我们提出了一个与动态难以意识到的加权损失（DAW）和双流式脱离学习架构（分离）的框架。受课程学习的启发，DAW通过适应性地测量难度从简单样本学习到困难样本。分离分离分级任务的特征，以避免潜在地强调偏见。随着DAW和Decarch的添加，该模型学习了鲁棒的分离特征表示形式，以探索DR和DME之间的内部相关性并实现更好的分级性能。在三个基准上进行的实验显示了我们框架内框架和跨数据库测试的有效性和鲁棒性。
Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of permanent blindness worldwide. Designing an automatic grading system with good generalization ability for DR and DME is vital in clinical practice. However, prior works either grade DR or DME independently, without considering internal correlations between them, or grade them jointly by shared feature representation, yet ignoring potential generalization issues caused by difficult samples and data bias. Aiming to address these problems, we propose a framework for joint grading with the dynamic difficulty-aware weighted loss (DAW) and the dual-stream disentangled learning architecture (DETACH). Inspired by curriculum learning, DAW learns from simple samples to difficult samples dynamically via measuring difficulty adaptively. DETACH separates features of grading tasks to avoid potential emphasis on the bias. With the addition of DAW and DETACH, the model learns robust disentangled feature representations to explore internal correlations between DR and DME and achieve better grading performance. Experiments on three benchmarks show the effectiveness and robustness of our framework under both the intra-dataset and cross-dataset tests.
