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Deep Learning: Artificial Intelligence (ai) Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Artificial Intelligence (AI)

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  • There are a number of ways that the field of deep learning has been characterized. Deep learning is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised).are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher level features are derived from lower level features to form a hierarchical representation.
are part of the broader machine learning field of learning representations of data.learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.

 Wikipedia
  • Deep learning algorithms are based on distributed representations. The underlying assumption behind distributed representations is that observed data are generated by the interactions of factors organized in layers. Deep learning adds the assumption that these layers of factors correspond to levels of abstraction or composition. Varying numbers of layers and layer sizes can be used to provide different amounts of abstraction.
 Wikipedia
  • Situational Awareness
    Situation awareness involves being aware of what is happening in the vicinity to understand how information, events, and one's own actions will impact goals and objectives, both immediately and in the near future. One with an adept sense of situation awareness generally has a high degree of knowledge with respect to inputs and outputs of a system, an innate "feel" for situations, people, and events that play out because of variables the subject can control. Lacking or inadequate situation awareness has been identified as one of the primary factors in accidents attributed to human error.[1] Thus, situation awareness is especially important in work domains where the information flow can be quite high and poor decisions may lead to serious consequences (such as piloting an airplane, functioning as a soldier, or treating critically ill or injured patients).
  • “For the biomedical image computing, machine learning, and bioinformatics scientists, the aforementioned challenges will present new and exciting opportunities for developing new feature analysis and machine learning opportunities. Clearly though, the image computing community will need to work closely with the pathology community and potentially whole slide imaging and microscopy vendors to be able to develop new and innovative solutions to many of the critical image analysis challenges in digital pathology.”


    Image analysis and machine learning in digital pathology: Challenges and opportunities.
Madabhushi A, Lee G
Med Image Anal. 2016 Oct;33:170-5.
  • OBJECTIVE. The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, su- pervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to brie y describe ethical dilemmas and legal risk. 

    CONCLUSION. Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement. 


    Implementing Machine Learning in Radiology Practice and Research 
Kohli M et al.
AJR 2017; 208:754–760
  • “ML comprises a broad class of statistical analysis algorithms that iteratively improve in response to training data to build models for autonomous predictions. In other words, computer program performance improves automatically with experience . The goal of an ML algorithm is to develop a mathematic model that is the data. Once this model is known data, it can be used to predict the labels of new data. Because radiology is inherently a data interpretation profession in extracting features from images and applying a large knowledge base to interpret those features—it provides ripe opportunities to apply these tools to improve practice.”


    Implementing Machine Learning in Radiology Practice and Research 
Kohli M et al.
AJR 2017; 208:754–760
    










  • Implementing Machine Learning in Radiology Practice and Research 
Kohli M et al.
AJR 2017; 208:754–760


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Implementing Machine Learning in Radiology Practice and Research 
Kohli M et al.
AJR 2017; 208:754–760
    










  • Implementing Machine Learning in Radiology Practice and Research 
Kohli M et al.
AJR 2017; 208:754–760
  • “Most ML relevant to radiology is super- vised. In supervised ML, data are labeled before the model is trained. For example, in training a project to identify a specific brain tumor type, the label would be tumor pathologic results or genomic information. These labels, also known as ground truth, can be as specific or general as needed to answer the question. The ML algorithm is exposed to enough of these labeled data to allow them to morph into a model designed to answer the question of interest. Because of the large number of well-labeled images required to train models, curating these datasets is often laborious and expensive.”

    
Implementing Machine Learning in Radiology Practice and Research 
Kohli M et al.
AJR 2017; 208:754–760
  • “ML encompasses many powerful tools with the potential to dramatically increase the information radiologists extract from images. It is no exaggeration to suggest the tools will change radiology as dramatically as the advent of cross-sectional imaging did. We believe that owing to the narrow scope of existing applications of ML and the complexity of creating and training ML models, the possibility that radiologists will be replaced by machines is at best far in the future. Successful application of ML to the radiology domain will require that radiologists extend their knowledge of statistics and data science to supervise and correctly interpret ML-derived results.”

    
Implementing Machine Learning in Radiology Practice and Research 
Kohli M et al.
AJR 2017; 208:754–760
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  • “This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on.”


    Deep Learning in Medical Image Analysis.
Shen D, Wu G, Suk HI
Annu Rev Biomed Eng. 2017 (in press)

  • Unlike in the fields of medicine and health, in the field of artificial intelligence and machine learning, the term validation often refers to the fine-tuning stage of model development, and another term, test, is used instead to mean the process of verifying model performance. 


    Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction 
Park SH, Han K
Radiology 2018; 286:800–809
  • “Evaluation of the clinical performance of a diagnostic or predictive artificial intelligence model built with high-dimensional data requires use of external data from a clinical cohort that ade- quately represents the target patient population to avoid over-estimation of the results due to over fitting and spectrum bias.” 


    Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction 
Park SH, Han K
Radiology 2018; 286:800–809
  • “The ultimate clinical verification of a diagnostic or predictive artificial intelligence tools requires a demonstration of their value through effect on patient outcomes, beyond performance metrics; this can be achieved through clinical trials or well- designed observational outcome research.”


    Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction 
Park SH, Han K
Radiology 2018; 286:800–809
  • “Fistulas should be described by the 2 epithelial structures they connect (eg, enteroenteric, enterocolic, enterocutaneous, rectovaginal, or enterovesical). Enteric fistulas within the abdominal cavity should be described as simple or complex similar to perianal fistulas . Complex, asterisk-shaped fistula complexes are often seen that tether multiple loops of small bowel and/or colon.”


    Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction 
Park SH, Han K
Radiology 2018; 286:800–809
  • “Artificial intelligence is the branch of computer science devoted to creating systems to perform tasks that ordinarily require human intelligence. This is a broad umbrella term encompassing a wide variety of subfields and techniques; in this article, we focus on deep learning as a type of machine learning.”

    
Deep Learning: A Primer for Radiologists
Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “Machine learning is the subfield of arti cial intelligence in which algorithms are trained to perform tasks by learning patterns from data rather than by explicit programming. In classic machine learning, expert humans discern and encode features that appear distinctive in the data, and statistical techniques are used to organize or segregate the data on the basis of these features.”


    Deep Learning: A Primer for Radiologists
Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “AI using deep learning demonstrates promise for detecting critical findings at noncontrast-enhanced head CT. A dedicated algorithm was required to detect SAI. Detection of SAI showed lower sensitivity in comparison to detection of HMH, but showed reasonable performance. Findings sup- port further investigation of the algorithm in a controlled and prospective clinical setting to determine whether it can independently screen noncontrast-enhanced head CT examinations and notify the interpreting radiologist of critical findings.”


    Automated Critical Test Findings identification and Online notification system Using artificial intelligence in imaging 
Prevedello LM et al.
Radiology (in press

  • “To evaluate the performance of an arti cial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or hydrocephalus (HMH) at non—contrast material–enhanced head computed tomo- graphic (CT) examinations and to determine algorithm performance for detection of suspected acute infarct (SAI).”


    Automated Critical Test Findings identification and Online notification system Using artificial intelligence in imaging 
Prevedello LM et al.
Radiology (in press
  • “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.”

    
Toolkits and Libraries for Deep Learning 
Bradley J. Erickson et al. 
J Digit Imaging (2017) 30:400–405
  • “Even more exciting is the finding that in some cases, computers seem to be able to “see” patterns that are beyond human perception.This discovery has led to substantial and increased interest in the field of machine learning— specifically, how it might be applied to medical images.”


    Machine Learning for Medical Imaging 
 Bradley J. Erickson et al.
 RadioGraphics 2017 (in press)

  • “These algorithms have been used for several challenging tasks, such as pulmonary embolism segmentation with computed tomographic (CT) angiography (3,4), polyp detection with virtual colonoscopy or CT in the setting of colon cancer (5,6), breast cancer detection and diagnosis with mammography (7), brain tumor segmentation with magnetic resonance (MR) imaging (8), and detection of the cognitive state of the brain with functional MR imaging to diagnose neurologic disease (eg, Alzheimer disease).”


    Machine Learning for Medical Imaging 
 Bradley J. Erickson et al.
 RadioGraphics 2017 (in press)
  • “If the algorithm system optimizes its parameters such that its performance improves—that is, more test cases are diagnosed correctly—then it is considered to be learning that task.”



    Machine Learning for Medical Imaging 
 Bradley J. Erickson et al.
 RadioGraphics 2017 (in press)
  • “Training: The phase during which the ma- chine learning algorithm system is given labeled example data with the answers (ie, labels)—for example, the tumor type or correct boundary of a lesion.The set of weights or decision points for the model is updated until no substantial improvement in performance is achieved.”


    Machine Learning for Medical Imaging 
 Bradley J. Erickson et al.
 RadioGraphics 2017 (in press)
  • “Deep learning, also known as deep neural network learning, is a new and popular area of research that is yielding impressive results and growing fast. Early neural networks were typi- cally only a few (<5) layers deep, largely because the computing power was not sufficient for more layers and owing to challenges in updating the weights properly. Deep learning refers to the use of neural networks with many layers—typically more than 20.”


    Machine Learning for Medical Imaging 
 Bradley J. Erickson et al.
 RadioGraphics 2017 (in press)
  • “CNNs are similar to regular neural networks. The difference is that CNNs assume that the inputs have a geometric relationship—like the rows and columns of images. The input layer of a CNN has neurons arranged to produce a convolution of a small image (ie, kernel) withthe image.This kernel is then moved across the image, and its output at each location as it moves across the input image creates an output value. Although CNNs are so named because of the convolution kernels, there are other important layer types that they share with other deep neural networks. Kernels that detect important features (eg, edges and arcs) will have large outputs that contribute to the final object to be detected.”


    Machine Learning for Medical Imaging 
 Bradley J. Erickson et al.
 RadioGraphics 2017 (in press)
  • “Machine learning is already being applied in the practice of radiology, and these applications will probably grow at a rapid pace in the near future.The use of machine learning in radiology has important implications for the practice of medicine, and it is important that we engage this area of research to ensure that the best care is afforded to patients. Understanding the properties of machine learning tools is critical to ensuring that they are applied in the safest and most effective manner.”


    Machine Learning for Medical Imaging 
 Bradley J. Erickson et al.
 RadioGraphics 2017 (in press)
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