Hello World Deep Learning in Medical Imaging

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.     

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.     

隐结构模型在慢性胃炎辨证中的应用探索——基于EM算法的因子分析方法

名老中医在长年的临床实践中积累了大量的宝贵经验,而这些经验都隐含在众多的临床病历中,机器学习是挖掘出这些隐含经验非常有效的工具,因此利用机器学习技术挖掘出隐含在大量病历资料中的临床经验,对于名老中医经验传承具有非常重要的价值。隐结构模型是张连文教授提出的一种模型,它能够较好地符合中医辨证理论。本文在其方法上进行了一定的简化和改进,并应用于慢性胃炎辨证。主要是采用基于EM（expectation maximum,最大期望）算法的因子分析方法处理病案数据,从而得到慢性胃炎辨证的隐结构,提高了学习速度和模型的准确性。     

人工智能在医学领域的应用综述

本文在概述人工智能相关知识的前提下，以患者就诊流程为线索，在整个医院管理为基础的大环境中从就医前的智能诊断，到就医时使用的医疗设备，再到就医后的健康管理进行概述。分析目前我国人工智能在医学领域中存在的技术、人才、管理标准、政策伦理问题，并给出符合未来医学领域人工智能发展的建议。     

The future of pathology is digital

Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of cases’ referral information, as needed for example, by the pathologist, and this support will be increased in the future even more.In radiology, established standards define information models, data transmission mechanisms, and workflows. Other disciplines, such as pathology, cardiology, and radiation therapy, now define further demands in addition to these established standards. Pathology may have the highest technical demands on the systems, with very complex workflows, and the digitization of slides generating enormous amounts of data up to Gigabytes per biopsy. This requires enormous amounts of data to be generated per biopsy, up to the gigabyte range.Digital pathology allows a change from classical histopathological diagnosis with microscopes and glass slides to virtual microscopy on the computer, with multiple tools using artificial intelligence and machine learning to support pathologists in their future work.     

Prognostic Bayesian networks I: rationale, learning procedure, and clinical use

Prognostic models are tools to predict the future outcome of disease and disease treatment, one of the fundamental tasks in clinical medicine. This article presents the prognostic Bayesian network (PBN) as a new type of prognostic model that builds on the Bayesian network methodology, and implements a dynamic, process-oriented view on prognosis. A PBN describes the mutual relationships between variables that come into play during subsequent stages of a care process and a clinical outcome. A dedicated procedure for inducing these networks from clinical data is presented. In this procedure, the network is composed of a collection of local supervised learning models that are recursively learned from the data. The procedure optimizes performance of the network’s primary task, outcome prediction, and handles the fact that patients may drop out of the process in earlier stages. Furthermore, the article describes how PBNs can be applied to solve a number of information problems that are related to medical prognosis.     

消化道内窥镜图像异常的人工智能诊断方法研究进展

随着深度学习的出现,图像处理不再局限于人工提取特征,转而对图像进行端到端的预测,实现了人工智能在图像处理领域的又一历史性飞越。作为人工智能医疗领域的热点应用,内镜图像异常检测能够准确快速地筛选整个消化道的异常,为医生提供诊断帮助。该文围绕消化道图像最为常见的息肉、出血、溃疡等异常,对其智能诊断方法展开研究,并探讨机器学习在消化内镜异常检测的应用现状,最后展望了未来消化道内窥镜病灶智能诊断的研究方向。     

Survey on the use of smart and adaptive engineering systems in medicine

In this paper, the current published knowledge about smart and adaptive engineering systems in medicine is reviewed. The achievements of frontier research in this particular field within medical engineering are described. A multi-disciplinary approach to the applications of adaptive systems is observed from the literature surveyed. The three modalities of diagnosis, imaging and therapy are considered to be an appropriate classification method for the analysis of smart systems being applied to specified medical sub-disciplines. It is expected that future research in biomedicine should identify subject areas where more advanced intelligent systems could be applied than is currently evident. The literature provides evidence of hybridisation of different types of adaptive and smart systems with applications in different areas of medical specifications.     

Computer systems for three-dimensional diagnostic imaging: an examination of the state of the art

This survey reviews three-dimensional (3D) medical imaging machines and 3D medical imaging operations. The survey is designed to provide a snapshot overview of the present state of computer architectures for 3D medical imaging. The basic volume manipulation, object segmentation, and graphics operations required of a 3D medical imaging machine are described and sample algorithms are presented. The architecture and 3D imaging algorithms employed in 11 machines which render medical images are assessed. The performance of the machines is compared across several dimensions, including image resolution, elapsed time to form an image, imaging algorithms employed in the machine, and the degree of parallelism employed in the architecture. The innovation in each machine, whether architectural or algorithmic, is described in detail. General trends for future developments in this field are delineated and an extensive bibliography is provided.     

医学图像语义概念识别方法研究

医学图像语义概念识别是医学图像知识表示的重要技术环节。研究医学图像语义概念识别方法,有助于机器理解和学习医学图像中的潜在医学知识,在影像辅助诊断和智能读片等应用中发挥重要作用。将医学图像的高频概念识别问题转化为多标签分类任务,利用基于卷积神经网络的深度迁移学习方法,识别有限数量的高频医学概念;同时利用基于图像检索的主题建模方法,从给定医学图像的相似图像中提取语义相关概念。国际跨语言图像检索论坛ImageCLEF于2018年5月组织ImageCLEFcaption 2018评测,其子任务“概念检测”的目标是给定222 314张训练图片和9 938张测试图片,识别111 156个语义概念。上述两种方法的实验结果已被提交。实验结果表明,利用基于卷积神经网络的深度迁移学习方法识别医学图像高频概念,F1值为0.092 8,在提交团队中排名第二;基于图像检索的主题模型可召回部分低频相关概念,F1值为0.090 7,然而其性能依赖于图像检索结果的质量。基于卷积神经网络的深度迁移学习方法识别医学图像高频概念的鲁棒性优于基于图像检索方法的鲁棒性,但在大规模开放语义概念的识别技术研究上仍需进一步完善。     

人工智能在中医药的研究现状及展望

中医药在世界范围内难以被广泛接受的主要问题是缺少足够客观定量的数据支撑和完备而自洽的理论体系。此外,传统中医术语的模糊性、理论知识的难以理解性、治疗思维的抽象性、中医医案的繁杂性也对中医现代化发展带来了极大的挑战。随着信息技术的日新月异,大数据和人工智能技术为规范中医诊疗数据、构建智能中医诊疗体系以及突破传统中医诊疗模式提供了新方法,进一步推动了中医药智能化的发展。经过半个多世纪的发展,中医与人工智能技术的融合逐步深入,取得了一定的应用成果,如中医药中医信息化数据库、中医四诊采集设备、中医辅助诊疗系统以及智慧中医健康管理等。但就目前而言,中医药智能化发展中仍存在数据标准欠缺、相关制度不够完善、交叉人才匮乏等问题,未来还需进一步建立规范的数据标准,完善数据共享、知识产权、伦理规范等法律法规,加速培养学科交叉复合型人才,创新思维革新医疗模式等,促进人工智能背景下的中医药创新发展。本文从知识发现与机器学习的角度,对人工智能在中医药的研究进展概括总结,以期为中医药智能化提供助力。     

Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts

Machine learning (ML) is becoming an integral aspect of several domains in medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such tools and are unprepared for their inevitable integration. To bridge this knowledge gap, we present an overview of key elements within this emerging data science discipline. First, we will cover general, well-established concepts within ML, such as data type concepts, data preprocessing methods, and ML study design. We will describe common supervised and unsupervised learning algorithms and their associated common machine learning terms (provided within a comprehensive glossary of terms that are discussed within this review). Overall, this review will offer a broad overview of the key concepts and algorithms in machine learning, with a focus on pathology and laboratory medicine. The objective is to provide an updated useful reference for those new to this field or those who require a refresher.     

Elicitation of neurological knowledge with argument-based machine learning

ObjectiveThe paper describes the use of expert's knowledge in practice and the efficiency of a recently developed technique called argument-based machine learning (ABML) in the knowledge elicitation process. We are developing a neurological decision support system to help the neurologists differentiate between three types of tremors: Parkinsonian, essential, and mixed tremor (comorbidity). The system is intended to act as a second opinion for the neurologists, and most importantly to help them reduce the number of patients in the “gray area” that require a very costly further examination (DaTSCAN). We strive to elicit comprehensible and medically meaningful knowledge in such a way that it does not come at the cost of diagnostic accuracy.Materials and methodsTo alleviate the difficult problem of knowledge elicitation from data and domain experts, we used ABML. ABML guides the expert to explain critical special cases which cannot be handled automatically by machine learning. This very efficiently reduces the expert's workload, and combines expert's knowledge with learning data. 122 patients were enrolled into the study.ResultsThe classification accuracy of the final model was 91%. Equally important, the initial and the final models were also evaluated for their comprehensibility by the neurologists. All 13 rules of the final model were deemed as appropriate to be able to support its decisions with good explanations.ConclusionThe paper demonstrates ABML's advantage in combining machine learning and expert knowledge. The accuracy of the system is very high with respect to the current state-of-the-art in clinical practice, and the system's knowledge base is assessed to be very consistent from a medical point of view. This opens up the possibility to use the system also as a teaching tool.     

Evolving rule-based systems in two medical domains using genetic programming

OBJECTIVE: To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. MATERIAL: Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. METHODS: Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. RESULTS: Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. CONCLUSION: The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.     

Use of the computer in clinical medicine

This article describes the application of computers in clinical medicine and the experience gained by the Institute of Medical Computer Science when introducing computer systems into the clinics of the University of Vienna Medical School in the last 20 years. It is shown what dramatic development has taken place in these years. The medical information system WAMIS with its central patient database is described as well as the medical record keeping documentation and retrieval system WAREL, which is destined to analyze medical natural language data. A further chapter deals with computers in clinical laboratories. At the end it is tried to point out future trends in applying computers in clinical medicine.     

产前超声医学图像处理技术综述

超声诊断是产科临床上应用最广泛的医学成像方式,与之相应的自动医学图像处理是提高诊断准确率与客观性的重要手段.本文首先介绍超声医学图像处理方法的原理及特点,讨论了若干技术及其涉及的算法,包括图像滤波、图像分割及机器学习技术等,浅析了设计可靠的超声医学图像处理方法的要点.其次,以产前超声医学为背景介绍了这些技术的应用,主要包括标准切面自动提取和生物学参数自动测量.最后,讨论了产前超声智能化诊断的发展方向.     

Toolkits and Libraries for Deep Learning

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.     

Multivariate image analysis in biomedicine

In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.