Abstract: A computerized efficient data processing management method for imaging applications first performs a data flow graph generation by computing means using at least one image data and at least one requested task to generate a data flow graph. The method then applies a task execution scheduling using the data flow graph generated, a caching system configuration, the at least one image data and at least one requested task to schedule execution of the at least one requested task to generate task execution output. In addition, an adaptive data processing method performs caching system update and an optimal data processing method further performs data flow graph update.

Abstract: A computerized model compatibility regulation method for imaging applications first performs a target domain B application by computing means using at least one image X and target domain B image analytics to generate a target domain B application output for X. The method then applies a reference domain A application by computing means to generate reference domain A application output for X. The method further performs a compatibility assessment to generate at least one compatibility result for X. In addition, the method checks the compatibility result for X and if the check output is incompatible, the method performs online correction to generate a corrected application output for X.

Abstract: A computerized prediction guided learning method for classification of sequential data performs a prediction learning and a prediction guided learning by a computer program of a computerized machine learning tool. The prediction learning uses an input data sequence to generate an initial classifier. The prediction guided learning may be a semantic learning, an update learning, or an update and semantic learning. The prediction guided semantic learning uses the input data sequence, the initial classifier and semantic label data to generate an output classifier and a semantic classification. The prediction guided update learning uses the input data sequence, the initial classifier and label data to generate an output classifier and a data classification. The prediction guided update and semantic learning uses the input data sequence, the initial classifier and semantic and label data to generate an output classifier, a semantic classification and a data classification.

Abstract: A computerized method of deep model matching for image transformation includes inputting pilot data and pre-trained deep model library into computer memories; performing a model matching scoring using the pilot data and the pre-trained deep model library to generate model matching score; and performing a model matching decision using the model matching score to generate a model matching decision output. Additional pilot data may be used to perform the model matching scoring and the model matching decision iteratively to obtain improved model matching decision output. Alternatively, the pre-trained deep model library may be pre-trained deep adversarial model library in the method.

Abstract: A computerized efficient data processing management method for imaging applications first performs a data flow graph generation by computing means using at least one image data and at least one requested task to generate a data flow graph. The method then applies a task execution scheduling using the data flow graph generated, a caching system configuration, the at least one image data and at least one requested task to schedule execution of the at least one requested task to generate task execution output. In addition, an adaptive data processing method performs caching system update and an optimal data processing method further performs data flow graph update.

Abstract: A computerized domain matching image conversion method for transportable imaging applications first performs a target domain A to source domain B matching converter training by computing means using domain B training images and at least one domain A image to generate an A to B domain matching converter. The method then applies the A to B domain matching converter to a domain A application image to generate its domain B matched application image. The method further applies a domain B imaging application analytics to the domain B matched application image to generate an imaging application output for the domain A application image.

Abstract: A computerized model compatibility regulation method for imaging applications first performs a target domain B application by computing means using at least one image X and target domain B image analytics to generate a target domain B application output for X. The method then applies a reference domain A application by computing means to generate reference domain A application output for X. The method further performs a compatibility assessment to generate at least one compatibility result for X. In addition, the method checks the compatibility result for X and if the check output is incompatible, the method performs online correction to generate a corrected application output for X.

Abstract: A computerized method of artifact regulation in deep model training for image transformation first performs one cycle of deep model training by computing means using a training data, a validation data, a similarity loss function, an artifact regulation loss function and a weight of loss functions to generate similarity loss and artifact regulation loss and a deep model. The method then performs a training evaluation using the similarity loss and the artifact regulation loss thus obtained to generate a training readiness output. Then, depending upon the training readiness output, the method may be terminated if certain termination criteria are met, or may perform another cycle of deep model training and training evaluation, with or without updating the weight, until the termination criteria are met. Alternatively, the deep model training in the method may be a deep adversarial model training or a bi-directional deep adversarial training.

Abstract: A computerized domain matching image conversion method for transportable imaging applications first performs a target domain A to source domain B matching converter training by computing means using domain B training images and at least one domain A image to generate an A to B domain matching converter. The method then applies the A to B domain matching converter to a domain A application image to generate its domain B matched application image. The method further applies a domain B imaging application analytics to the domain B matched application image to generate an imaging application output for the domain A application image.

Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Abstract: A computerized method of artifact regulation in deep model training for image transformation first performs one cycle of deep model training by computing means using a training data, a validation data, a similarity loss function, an artifact regulation loss function and a weight of loss functions to generate similarity loss and artifact regulation loss and a deep model. The method then performs a training evaluation using the similarity loss and the artifact regulation loss thus obtained to generate a training readiness output. Then, depending upon the training readiness output, the method may be terminated if certain termination criteria are met, or may perform another cycle of deep model training and training evaluation, with or without updating the weight, until the termination criteria are met. Alternatively, the deep model training in the method may be a deep adversarial model training or a bi-directional deep adversarial training.

Abstract: A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image integration performs a deep image integration learning on multi-modality training images and corresponding desired integrated images to generate a deep image integration model, which is applied to transform multi-modality images into a high quality integrated image mimicking a desired integrated image.

Abstract: A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image prediction performs a deep image prediction learning on universal modality training images and corresponding desired modality prediction images to generate a deep image prediction model, which is applied to transform universal modality images into a high quality image mimicking a desired modality prediction image.

Abstract: A computerized method of deep model matching for image transformation includes inputting pilot data and pre-trained deep model library into computer memories; performing a model matching scoring using the pilot data and the pre-trained deep model library to generate model matching score; and performing a model matching decision using the model matching score to generate a model matching decision output. Additional pilot data may be used to perform the model matching scoring and the model matching decision iteratively to obtain improved model matching decision output. Alternatively, the pre-trained deep model library may be pre-trained deep adversarial model library in the method.

Abstract: A computerized automated parameterization image pattern detection and classification method performs (1) morphological metrics learning using labeled region data to generate morphological metrics; (2) intensity metrics learning using learning image and labeled region data to generate intensity metrics; and (3) population learning using the morphological metrics and the intensity metrics to generate learned pattern detection parameter. The method may further update the learned pattern detection parameter using additional labeled region data and learning image, and apply pattern detection with optional user parameter adjustment to image data to generate detected pattern. The method may alternatively perform pixel parameter learning and pixel classification to generate pixel class confidence, and uses the pixel class confidence and the labeled region data to perform pattern parameter learning to generate the learned pattern detection parameter.

Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Abstract: A computerized efficient machine learning method for classification of data and new class discovery inputs labeled data and unlabeled data into a computer memory for a computerized machine tool to perform (a) initial supervised learning using the labeled data to generate a classifier, (b) semi-supervised learning using the labeled data, the classifier and the unlabeled data to generate an updated classifier and high confidence data, (c) active learning using the updated classifier and the unlabeled data to generate a data label request and receive new class labeled data to generate augmented labeled data, (d) new class discovery using the updated classifier and the data label request to generate data of potential new classes and receive labels for potential new class data to generate new class labeled data, and (e) supervised learning using the high confidence data, the labeled data and the augmented labeled data to generate an output classifier.

Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Abstract: A computerized method of automated hyper-parameterization for image-based deep model learning performs a deep model setup learning using initial learning images, initial truth data and a hyper-parameter setup recipe to generate deep model setup parameters, then performs a deep model learning using learning images, truth data and the generated deep model setup parameters to generate a deep model. Alternatively, the deep model learning may be a guided deep model learning. The deep model setup learning performs a deep model application, a deep quantifier calculation, and a salient hyper-parameter prediction. The hyper-parameter setup recipe may be generated by performing (a) a deep hyper-parameter mapping using application-specific learning images and application-specific truth data, (b) a salient hyper-parameter extraction, (c) a deep quantifier generation, and (d) a salient hyper-parameter prediction learning.

Abstract: A computerized automated parameterization image pattern detection and classification method performs (1) morphological metrics learning using labeled region data to generate morphological metrics; (2) intensity metrics learning using learning image and labeled region data to generate intensity metrics; and (3) population learning using the morphological metrics and the intensity metrics to generate learned pattern detection parameter. The method may further update the learned pattern detection parameter using additional labeled region data and learning image, and apply pattern detection with optional user parameter adjustment to image data to generate detected pattern. The method may alternatively perform pixel parameter learning and pixel classification to generate pixel class confidence, and uses the pixel class confidence and the labeled region data to perform pattern parameter learning to generate the learned pattern detection parameter.