High-performance deep neural networks for medical image analysis

项目摘要 深度神经网络(DNN)缺乏透明度和可信性，长期以来一直被认为是主要的 这项技术的缺陷，阻碍了它在许多实际应用中的广泛接受。目标是 本项目的目的是建立一种新的对比特征分析(CFA)框架，用于可靠地显示 用于医学图像分析的高维特征空间和高性能DNN的有效设计。我们 假设基于CFA的特征可视化将使我们能够量化特征空间的质量 在DNN的训练/测试过程中使用不同的层，并为我们提供有效的工具来修剪网络 用于增强性能的架构。具体来说，我们将(1)开发一种高效的可视化技术CFA 对于高维要素数据，2)应用CFA可视化框架自动求精DNN 提高性能的架构，以及3)展示CFA在解决临床问题方面的潜力 有问题。该项目的成功完成将使我们能够可靠地分析要素数据并进行量化 DNN的不同层上的特征空间的质量。这项研究还承诺提供高性能 用于医学图像分析的DNN显著提高了基于人工智能的诊断、预后和治疗 规划不同的疾病。

Project Summary Lack of transparency and trustworthiness of deep neural networks (DNNs) has long been recognized as a major drawback of the technology, hindering its widespread acceptance in many practical applications. The objective of this project is to establish a novel contrastive feature analysis (CFA) framework for reliable visualization of the high dimensional feature space and effective design of high-performance DNNs for medical image analysis. We hypothesize that CFA-based feature visualization will enable us to quantify the quality of the feature space at different layers during training/testing of a DNN and empower us with an effective tool to prune the network architecture for enhanced performance. Specifically, we will (1) develop an efficient visualization technique CFA for high dimensional feature data, 2) apply the CFA visualization framework to automatically refine DNN architecture for improved performance, and 3) demonstrate the potential of CFA in solving clinical problems. Successful completion of the project will enable us to analyze the feature data reliably and quantify the quality of the feature space at different layers of a DNN. The study also promises to provide high-performance DNNs for medical image analysis to substantially improve the AI-based diagnosis, prognosis and treatment planning of different diseases.