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 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 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: 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.

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.