论文标题
通过简单的启发式方法，用于对底面图像进行二元分类的最佳转移学习模型
Optimal Transfer Learning Model for Binary Classification of Funduscopic Images through Simple Heuristics
论文作者
论文摘要
深度学习模型具有从根本上彻底改变医学成像分析的能力，并且在计算机辅助诊断中特别有趣。我们尝试使用深度学习的神经网络来诊断眼睛内部的视觉图像。最近，一些健壮的深度学习方法进行了二元分类，以推断出特定的眼部疾病（例如青光眼或糖尿病性视网膜病）的存在。为了扩大计算机辅助眼疾病诊断的应用，我们提出了疾病分类的统一模型：底眼图像的低成本推断，以确定其是健康还是患病。为了实现这一目标，我们使用转移学习技术，该技术保留了预训练的基础体系结构的更总体功能，但可以适应另一个数据集。为了进行比较，我们然后开发一个自定义启发式方程和评估度量排名系统，以确定最佳的基础体系结构和超参数。 X Ception Base架构，ADAM优化器和平方平方错误损耗函数的表现最佳，达到90％的精度，94％的灵敏度和86％的特异性。为了额外的使用，我们将模型包含在Web界面中，该模型可以访问该文件选择器的本地文件系统，从而可以在任何与Internet连接的设备上使用：移动，PC或其他方式。
Deep learning models have the capacity to fundamentally revolutionize medical imaging analysis, and they have particularly interesting applications in computer-aided diagnosis. We attempt to use deep learning neural networks to diagnose funduscopic images, visual representations of the interior of the eye. Recently, a few robust deep learning approaches have performed binary classification to infer the presence of a specific ocular disease, such as glaucoma or diabetic retinopathy. In an effort to broaden the applications of computer-aided ocular disease diagnosis, we propose a unifying model for disease classification: low-cost inference of a fundus image to determine whether it is healthy or diseased. To achieve this, we use transfer learning techniques, which retain the more overarching capabilities of a pre-trained base architecture but can adapt to another dataset. For comparisons, we then develop a custom heuristic equation and evaluation metric ranking system to determine the optimal base architecture and hyperparameters. The Xception base architecture, Adam optimizer, and mean squared error loss function perform best, achieving 90% accuracy, 94% sensitivity, and 86% specificity. For additional ease of use, we contain the model in a web interface whose file chooser can access the local filesystem, allowing for use on any internet-connected device: mobile, PC, or otherwise.
