Dave's PCF WIP: Paragraphs

Paragraphs

Actions	Matter	Content	Para #	Notes	Modified
View Edit Delete	PER-17	Deep learning can include machine learning algorithms which can use multiple layers of non-linear processing units or elements. Each layer can use the output from a higher layer as input. Deep learning systems can work in a supervised setting, e.g. using one more classification systems. Deep learning systems can also work in an unsupervised setting, e.g. in the context of texture analysis or pattern recognition. Deep learning systems can learn multiple levels of representations that correspond to different levels of abstraction. The different levels can form an order or a hierarchy of concepts. The different layers of a deep learning system can reside in different layers of an artificial neural network, i.e. a deep neural network. They can include hidden layers in an ANN. Deep learning systems and deep ANNs can utilize Boltzmann machines. With deep learning systems, layers can correspond to layers of abstraction, e.g. across a deep neural network. Varying numbers of layers and layer sizes can provide different degrees of abstraction. Higher level, more complex concepts can be learned from lower level layers.	86	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Deep neural networks (DNNs) can be one or more ANNs with multiple hidden layers between the input and output layers. DNNs can model complex non-linear relationships. DNNs can generate models where the object is expressed as a layered composition. DNNs can be feedforward networks in which data flows from the input layer to the output layer without looping back, DNNs can be recurrent neural networks or convolutional deep neural networks.	87	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data can be more abundant than labeled data. For example, in a clinical environment, a deep learning system with a multi-layered ANN can initially be trained using a classification of outcomes in a supervised fashion. As the data grows, the system can optionally learn in an unsupervised manner, for example by utilizing pattern recognition across large clinical datasets, which can include pre-operative, intra-operative and post-operative data. (See US Patent 11,278,413 Para. 76-79).	88	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Classification	89	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Classification can be a process of creating categories, in which data or objects can be recognized, differentiated or understood. A classification system can be an approach of accomplishing classification. Classification can be performed using mathematical classification, statistical classification, classification theorems, e.g. in mathematics, and attribute value systems. Classifications can be alphanumeric. Classifications can be single or multi-dimensional. Classifications can be single or multi-layered. Classifications can be color coded. An ANN can use a single classification system, e.g. in supervised learning. An ANN can use multiple classification systems. When multiple classification systems are used, they can reside in different layers of a DNN or deep learning system.	90	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Classification can be the problem of identifying to which of a set of categories or sub-populations a new observation belongs; this can be determined, for example, using a training data set with observations whose category membership is known. For example, a diagnosis can be assigned to a patient as a category which can be described by measured data or characteristics such a heart rate, blood pressure, presence of absence of symptoms or combinations of symptoms. Classification can be a pattern recognition.	91	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Individual observations or data can be divided into a set of quantifiable properties. These properties may be categorical, e.g. “a”, “b”, “c”, “d” etc. or ordinal, e.g. “excellent”, “very good”, “good”, “acceptable”, “average”, or “poor”. They can be integer or real valued. Observations or data can also be classified using similarity or distance functions, e.g. based on earlier observations or data. An algorithm that implements classification can be a classifier, A classifier can sometimes also be a mathematical function, e.g. implemented by a classification algorithm, that can map input data to a category.	92	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Data can be classified, for example, into “excellent”, “good”, “acceptable” or “poor” outcome, e.g. one or more clinical outcomes or clinical outcome variables. Numeric values or ranges of numeric values can be assigned to different classes, for example numeric values or ranges of numeric values from a patient reported outcome measurement, from a clinical reporting system, and/or from one or more electronic measurements.	93	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Discriminative vs. Generative Models and Networks	94	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	In machine learning, discriminative models can be distinguished from generative models.	95	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	1. Discriminative models are trained to learn the boundaries between classes. They model the conditional probability of a target variable Y (class), given an observation x: P(Y\|X=x) (“probability of Y given X=x”). Discriminative models describe the probability for classifying a given example x into a classy E Y. Discriminative models include, for example, logistic regression, conditional random fields, support vector machines, neural networks, random forests, or perceptrons.	96	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Generative models model the distribution of individual classes. They can generate data and provide a statistical model of the joint probability distribution on X×Y, P(X,Y)=P(X\|Y)*P(Y), for an observable variable X and a target variable Y. Generative models include, for example, naïve Bayes models and Bayes networks, hidden Markov models, Boltzmann machines, variational autoencoders or generative adversarial networks (GAN).	97	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	In some embodiments, the computer system can use a trained artificial neural network (ANN) to determine the treatment plan. The ANN can implement a discriminative model. A discriminative model can be trained to classify the input data, i.e. the preoperative and/or intraoperative data and/or postoperative data, into different classes, wherein each class can represent a different treatment plan.	98	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	In some embodiments, the ANN can implement a generative model. Instead of assigning preoperative and/or intraoperative input data and/or postoperative data to an existing class, a generative model is trained to generate the treatment plan steps based on the input data.	99	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	In some embodiments, a generative and a discriminative network model can be combined into a generative adversarial network (GAN) to generate a treatment plan. Using a training data set of existing recorded treatment plans for a number of preoperative and/or intraoperative input data and/or postoperative data sets, in this situation, the generative network can be trained to generate a preferred treatment plan from the preoperative and/or intraoperative input data. The discriminative network can be trained to evaluate the generated treatment plan and to distinguish the generated treatment plan from the actual treatment plan of the training case. Thus, the discriminative network can force the generative network to improve its results. (See US Patent 11,278,413 Para. 80-91).	100	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	"Instruction" refers to a direction or order. (Search "instruction" on wordhippo.com. WordHippo, 2023. Web. Accessed 2 June 2023.) In certain embodiments, an instruction refers to one or more commands in a set of commands that direct a processor of a computing device to perform logic operations, input operations, output operations, or the like. Such instructions may exist in machine-readable and/or human readable formats. Examples of code include binary code, machine code, scripts, compiled code, virtual machine code, and the like. In certain embodiments, one or more instructions may be issued to a processor in response to a user action in relation to an item on a user interface.	101	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	"Instrument" refers to any apparatus, device, or object that can be used by a user. An instrument may be used for a specific or a generic purpose. An instrument may also be referred to as instrumentation. Instrumentation may refer to a single instrument and/or a plurality of instruments. An instrument may be specifically designed, constructed or fabricated for use by a specific user and/or for a single use. A patient specific instrument is one example of an instrument.	102	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	"Step" refers to a measure or action, especially one of a series taken for a given purpose or goal. (Search "step" on wordhippo.com. WordHippo, 2023. Web. Accessed 2 June 2023.)	103	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	As used herein, "segmentation" or "image segmentation" refers to the process of partitioning an image into different meaningful segments. These segments may correspond to different tissue classes, organs, pathologies, bones, or other biologically relevant structures. Medical image segmentation accommodates imaging ambiguities such as by low contrast, noise, and other imaging ambiguities.	104	Added by DJM Jan 2024	1/6/24, 10:05 PM
View Edit Delete	PER-17	Certain computer vision techniques can be used or adapted for image segmentation. For example, the techniques and or algorithms for segmentation may include, but are not limited to: Atlas-Based Segmentation: For many applications, a clinical expert can manually label several images; segmenting unseen images is a matter of extrapolating from these manually labeled training images. Methods of this style are typically referred to as atlas-based segmentation methods. Parametric atlas methods typically combine these training images into a single atlas image, while nonparametric atlas methods typically use all of the training images separately. Atlas-based methods usually require the use of image registration in order to align the atlas image or images to a new, unseen image.	105	Added by DJM Jan 2024	1/6/24, 10:05 PM

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