Abstract: A system (100) comprising one or more processors (110) and one or more storage devices (120) is configured to obtain biology-related image-based input data (107) and generate a high-dimensional representation of the biology-related image-based input data (107) by a trained visual recognition machine-learning algorithm executed by the one or more processors (110). The high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to at least one of store the high-dimensional representation of the biology-related image-based input data (107) together with the biology-related image-based input data (107) by the one or more storage devices (120) or output biology-related language-based output data (109) corresponding to the high-dimensional representation.

Abstract: A system (100) for processing biology-related data comprises one or more processors (110) coupled to one or more storage devices (120). The system (100) is configured to receive biology-related image-based search data (103) and configured to generate a first high-dimensional representation of the biology-related image-based search data (103) by a trained visual recognition machine-learning algorithm executed by the one or more processors (110). The first high-dimensional representation comprises at least 3 entries each having a different value. Further, the system (100) is configured to obtain a plurality of second high-dimensional representations (105) of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets.

Abstract: Embodiments relate to a system (100) comprising one or more processors (110) and one or more storage devices (120). The system (100) is configured to receive biology-related language-based search data (101) and generate a first high-dimensional representation of the biology-related language-based search data (101) by a trained language recognition ma-chine-learning algorithm executed by the one or more processors (110). The first high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to obtain a plurality of second high-dimensional representations (105) of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets and compare the first high-dimensional representation with each second high-dimensional representation of the plurality of second high-dimensional representations (105).

Abstract: An optical imaging device for a microscope comprises a first optical system configured to form a first optical image corresponding to a first region of a sample in accordance with a first imaging mode, a second optical system configured to form a second optical image corresponding to a second region of said sample, wherein said first and second regions spatially coincide in a target region of said sample and said first and second imaging modes are different from each other, a memory storing first distortion correction data suitable for correcting a first optical distortion caused by said first optical system in said first optical image, second distortion correction data suitable for correcting a second optical distortion caused by said second optical system in said second optical image, and transformation data suitable for correcting positional misalignment between said first and second optical images, and a processor which is configured to process first image data representing said first optical image based

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 SPIM-microscope (Selective Plane Imaging Microscopy) and a method of operating the same having a y-direction illumination light source and a z-direction detection light camera. An x-scanner generates a sequential light sheet by scanning the illumination light beam in the x-direction. An electronic zoom is provided that is adapted to change the scanning length in the x-direction independently of a focal length of the illumination light beam and a size of the light sheet in the y-direction and in the z-direction, wherein the number of image pixels in x-direction is maintained unchanged by the electronic zoom independently of the scanning length in x-direction that has been selected.

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 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.