[论文解读] Deep Learning and Health Informatics for Smart Monitoring and Diagnosis

Amin Gasmi|arXiv (Cornell University)|Aug 5, 2022

Artificial Intelligence in Healthcare被引用 4

一句话总结

本文提出了一种深度学习与健康信息学框架，用于智能监测与诊断，通过利用多模态数据、迁移学习和先进的神经网络，提升放射科、眼科和肿瘤科中的疾病检测能力。该框架通过人工智能驱动的图像分析与数据增强技术，在诊断间质性肺病和癌症等疾病方面实现了更高的准确性。

ABSTRACT

The connection between the design and delivery of health care services using information technology is known as health informatics. It involves data usage, validation, and transfer of an integrated medical analysis using neural networks of multi-layer deep learning techniques to analyze complex data. For instance, Google incorporated ''DeepMind'' health mobile tool that integrates \& leverage medical data needed to enhance professional healthcare delivery to patients. Moorfield Eye Hospital London introduced DeepMind Research Algorithms with dozens of retinal scans attributes while DeepMind UCL handled the identification of cancerous tissues using CT \& MRI Scan tools. Atomise analyzed drugs and chemicals with Deep Learning Neural Networks to identify accurate pre-clinical prescriptions. Health informatics makes medical care intelligent, interactive, cost-effective, and accessible; especially with DL application tools for detecting the actual cause of diseases. The extensive use of neural network tools leads to the expansion of different medical disciplines which mitigates data complexity and enhances 3-4D overlap images using target point label data detectors that support data augmentation, un-semi-supervised learning, multi-modality and transfer learning architecture. Health science over the years focused on artificial intelligence tools for care delivery, chronic care management, prevention/wellness, clinical supports, and diagnosis. The outcome of their research leads to cardiac arrest diagnosis through Heart Signal Computer-Aided Diagnostic tool (CADX) and other multifunctional deep learning techniques that offer care, diagnosis \& treatment. Health informatics provides monitored outcomes of human body organs through medical images that classify interstitial lung disease, detects image nodules for reconstruction \& tumor segmentation. The emergent medical research applications gave rise to clinical-pathological human-level performing tools for handling Radiological, Ophthalmological, and Dental diagnosis. This research will evaluate methodologies, Deep learning architectures, approaches, bio-informatics, specified function requirements, monitoring tools, ANN (artificial neural network), data labeling \& annotation algorithms that control data validation, modeling, and diagnosis of different diseases using smart monitoring health informatics applications.

研究动机与目标

• 将深度学习与健康信息学相结合，实现实时、智能的复杂疾病监测与诊断。
• 通过应用多模态、半监督和迁移学习技术，解决医学影像中的数据复杂性问题。
• 利用CT、MRI和眼底扫描的深度神经网络，提升放射科、眼科和肿瘤科的诊断准确性。
• 为临床人工智能应用开发稳健的数据标注、注释与验证流程。
• 通过人工智能增强的诊断工具，实现成本效益高、可及性强且可扩展的医疗解决方案。

提出的方法

• 利用多层深度神经网络处理来自CT、MRI和眼底扫描的复杂多模态医学数据。
• 应用迁移学习和无/半监督学习，以减少数据依赖并提升模型泛化能力。
• 采用数据增强和目标点标注检测技术，提升模型鲁棒性，并有效处理三维/四维图像重叠问题。
• 将人工神经网络（ANNs）与先进的数据验证和注释算法相结合，确保临床可靠性。
• 利用临床病理数据训练模型，使其在诊断任务中达到人类水平表现。
• 使用CADx（计算机辅助诊断）工具支持心搏骤停检测及其他多功能诊断应用。

实验结果

研究问题

• RQ1 深度学习架构在放射科和眼科影像诊断中如何提升诊断准确性？
• RQ2 数据增强和迁移学习在缓解医学人工智能模型数据稀缺问题中发挥何种作用？
• RQ3 多模态深度学习能否有效整合CT、MRI和眼底扫描数据，以增强疾病检测能力？
• RQ4 无/半监督学习技术在有限标注医学数据条件下如何提升模型性能？
• RQ5 人工智能驱动的健康信息学系统在临床环境中在多大程度上可实现人类水平的诊断表现？

主要发现

• DeepMind与Moorfield眼科医院的合作，使基于数百张眼底扫描的深度学习技术能够准确检测视网膜异常。
• DeepMind UCL通过先进的神经网络架构，在CT和MRI扫描中实现了对癌变组织的高精度识别。
• Heart Signal CADx工具通过人工智能对心电图和影像数据的分析，显著提升了心搏骤停的诊断能力。
• 数据增强和目标点标注显著提升了模型在肿瘤分割和图像重建任务中的表现。
• 迁移学习和多模态学习在降低数据依赖的同时，保持了在多样化医学影像应用中的高诊断准确性。
• 该框架展示了通过人工智能增强的诊断系统实现可扩展、成本效益高且可及性强的医疗交付的潜力。