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arXiv 2402.16865eess.IVcs.CVcs.LG

在机器学习架构中使用可学习概率离散隐变量增强眼部疾病检测

Enhance Eye Disease Detection using Learnable Probabilistic Discrete Latents in Machine Learning Architectures

National University Singapore（新加坡国立大学）；Asian Institute of Digital Finance（亚洲数字金融研究所）；School of Medicine（医学院）；College of Design & Engineering（设计与工程学院）

Anirudh Prabhakaran, YeKun Xiao, Ching-Yu Cheng, Dianbo Liu

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AI总结：

针对眼部疾病自动诊断中模型可靠性与不确定性估计的挑战，本研究提出将GFlowOut与ResNet18、ViT集成的方法，通过GFlowNets学习隐离散dropout掩码后验分布，提升了检测准确率与可解释性。

摘要：

包括糖尿病视网膜病变和青光眼在内的眼部疾病因其高患病率及导致视力损伤的潜力，构成了重大的公共卫生挑战。早期且准确的诊断对于有效的治疗和病情管理至关重要。近年来，深度学习模型已成为分析视网膜成像等医学图像的强大工具。然而，模型可靠性与不确定性估计方面仍存在挑战，这两点对临床决策至关重要。本研究利用生成流网络（Generative Flow Networks, GFlowNets）的概率框架，学习隐离散dropout掩码上的后验分布，以用于基于眼底图像的眼部疾病分类与分析。我们开发了一种鲁棒且具备泛化性的方法，将GFlowOut与ResNet18、ViT模型作为骨干网络集成，用于识别多种眼部病症。本研究采用一组独特的dropout掩码——无掩码、随机掩码、自底向上掩码和自顶向下掩码，以提升模型分析这些眼底图像的性能。我们的结果表明，可学习概率隐变量显著提升了准确率，表现优于传统dropout方法。我们使用梯度图计算方法Grad-CAM评估模型的可解释性，观察到模型能准确聚焦于图像的关键区域以进行预测。将GFlowOut集成到神经网络中为眼部疾病的自动诊断带来了颇具前景的进展，对改善临床工作流程和患者预后具有重要意义。

英文摘要：

Ocular diseases, including diabetic retinopathy and glaucoma, present a significant public health challenge due to their high prevalence and potential for causing vision impairment. Early and accurate diagnosis is crucial for effective treatment and management. In recent years, deep learning models have emerged as powerful tools for analysing medical images, such as retina imaging. However, challenges persist in model relibability and uncertainty estimation, which are critical for clinical decision-making. This study leverages the probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over latent discrete dropout masks for the classification and analysis of ocular diseases using fundus images. We develop a robust and generalizable method that utilizes GFlowOut integrated with ResNet18 and ViT models as the backbone in identifying various ocular conditions. This study employs a unique set of dropout masks - none, random, bottomup, and topdown - to enhance model performance in analyzing these fundus images. Our results demonstrate that our learnable probablistic latents significantly improves accuracy, outperforming the traditional dropout approach. We utilize a gradient map calculation method, Grad-CAM, to assess model explainability, observing that the model accurately focuses on critical image regions for predictions. The integration of GFlowOut in neural networks presents a promising advancement in the automated diagnosis of ocular diseases, with implications for improving clinical workflows and patient outcomes.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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