Abstract: Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.

Abstract: A container treatment apparatus for transporting plastic preforms along a predetermined transport path includes several transport devices, each of which has at least one holding element for holding a plastic container and guiding the plastic container along a portion of the transport path, wherein a second transport device immediately follows a first transport device along the transport path, and containers are transferred from a holding device of the first transport device to a holding device of the second transport device. The transport path along which the containers can be guided by the first transport device is arranged at least in portions outside a clean room, and the transport path along which the containers can be guided by the first transport device is surrounded at least in part by a radiation shielding device.

Abstract: A implement system for powersport vehicles have implements attachable to the powersport vehicles. The implements have a modular electric motor unit that may be toollessly attached to the implement providing operational power to the implement. A mower implement has a mower deck and a suspension assembly that provides a forward rearward pivot axis and a left right pivot axis when hitched to the powersport vehicle. A receiving region for the electric motor unit is positioned in the suspension assembly. The mower deck may be rotated about the left right pivot axis to an upright position and latched at the position. The single motor in the receiving region connecting to three mower blade pulleys for rotating three mower blades by the one electric motor unit utilizing a pulley system.

Abstract: Presented herein are systems and methods for semantic image retrieval. A computing system may identify a first biomedical image. The computing system may apply an image retrieval model to the first biomedical image. The image retrieval model may have a convolution block having a first plurality of parameters to generate a feature map using the first biomedical image. The first plurality of parameters may be transferred from a preliminary model. The image retrieval model may have an encoder having a second plurality of parameters to generate a first hash code for the first biomedical image based on the feature map. The computing system may select. from the plurality of second biomedical images corresponding to a plurality of second hash codes, a subset of second biomedical images using the first hash code. The computing system may provide the subset of second biomedical images identified using the first biomedical image.

Abstract: A first communication device that is configured to bidirectionally exchange data with a second communication device comprises circuitry that is configured to initiate a bidirectional transmit opportunity (TXOP) by transmitting, to the second communication device, a TXOP indicator indicating that a wideband TXOP is split into two channels and indicating that a first channel is primarily to be used for transmission of data from the first communication device to the second communication device and a second channel is primarily to be used for transmission of data from the second communication device to the first communication device. Further, the first communication device transmits data to the second communication device on the first channel and listen for the reception of data from the second communication device on the second channel.

Abstract: A service system for gas compartments with a gas treatment device for treating a gas present in at least one gas compartment, at least one sensor device for monitoring at least one gas property of the gas, at least one connection coupled to the gas treatment device and the at least one sensor device, which connection is intended for coupling to the gas compartment, at least one conveying device for conveying a gas from the gas compartment into the gas treatment device and from the gas treatment device at least indirectly back into the gas compartment, and at least one control unit which is connected at least to the sensor device and monitors and controls at least the conveying device and/or the gas treatment device.

Abstract: Systems and methods are disclosed for identifying a diagnostic feature of a digitized pathology image, including receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information, and/or patient information, applying a machine learning model to predict a plurality of relevant diagnostic features based on medical metadata, the machine learning model having been developed using an archive of processed images and prospective patient data, and determining at least one relevant diagnostic feature of the relevant diagnostic features for output to a display.

Abstract: Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.

Abstract: The present disclosure discusses systems and methods to detect blur in digital images. The solution can be incorporated into the quality control systems of pathology and other slide scanners or can be a stand-alone solution. The solution can identify scanned images that include blur and cause the scanner to automatically rescan the blurry image. The solution can also identify regions of the scanned image that include blur. The solution can generate blur maps for each of the scanned images that identify regions of the scanned image that include blur.

Abstract: A drive unit for motor vehicle applications, in particular to a locking system drive unit. The latter has an enclosure consisting substantially of a housing and cover. In addition, an electric motor is realized inside the enclosure. The electric motor is receiving with its two motor bearings in associated cutouts in the housing and is fixed by joining together the housing and the cover. According to the invention, the housing and/or the cover have/has fixing anchors in a central region that are arranged on both sides of the cutouts.

Abstract: Disclosed herein is a mixture of at least one semi-aromatic polyamide A) and at least one semi-aromatic polyamide B), both containing repeat units derived from terephthalic acid. Further disclosed herein are a polyamide molding composition including said mixture of polyamides, a molded article produced from said polyamide molding composition and a method of using the mixture of semi-aromatic polyamides for the production of a molded article with improved mechanical properties, in particular with improved weld line strength.

Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.

Abstract: Described herein are systems and methods of training models to segment images. A device may identify a training dataset. The training dataset may include images each having a region of interest. The training dataset may include first annotations. The device may train, using the training dataset, an image segmentation model having parameters to generate a corresponding first segmented images. The device may provide the first segmented images for presentation on a user interface to obtain feedback. The device may receive, via the user interface, a feedback dataset including second annotations for at least a subset of the first segmented images. Each of the second annotations may label at least a second portion of the region of interest in a corresponding image of the subset. The device may retrain, using the feedback dataset received via the user interface, the image segmentation model.

Abstract: Presented herein are systems and methods for detecting labels in biomedical images. A computing system having one or more processors coupled with memory may identify, from a data source, a biomedical image having a first plurality of pixels in a first color representation. The computing system may convert the first plurality of pixels from the first color representation to a second color representation to generate a second plurality of pixels. The computing system may identify, from the second plurality of pixels, a subset of pixels having a color value satisfying a threshold value. The computing system may detect the biomedical image as having at least one label based at least on a number of pixels in the subset of pixels satisfying a threshold count. The computing system may store, in one or more data structures, an indication for the biomedical image as having the at least one label.

Abstract: The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not.

Abstract: The present invention relates to abrasive zirconium corundum grains based on Al2O3 and ZrO2 that have been melted in an electrical arc furnace and have a content of—Al2O3 between 52% and 62% by weight,—ZrO2 (+HfO2) between 35.0% and 45.0% by weight, where a total of at least 80% by weight of the ZrO2, based on the total content of ZrO2, is in the tetragonal and/or cubic high-temperature modification,—Si compounds of more than 0.8% by weight, expressed as SiO2,—carbon between 0.03% and 0.5% by weight,—additives between 0.5% and 10% by weight and—raw material-related impurities of less than 3% by weight, where the raw material base for the abrasive grains comprises aluminum oxide, baddeleyite and zircon sand.

Abstract: Systems and methods for processing digital medical images to infer metadata from those images are disclosed. In some aspects, digital medical images may be processed to infer metadata by receiving a plurality of digital medical images, receiving a prompt, the prompt being a request for a specific type of metadata to be inferred from the plurality of digital medical images, determining, using a trained foundation model, at least one feature descriptor from the plurality of digital medical images based on the prompt, and providing for output the at least one feature descriptor for each of the plurality of digital medical images.

Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.

Abstract: Systems and methods are disclosed for processing digital images to predict at least one continuous value comprising receiving one or more digital medical images, determining whether the one or more digital medical images includes at least one salient region, upon determining that the one or more digital medical images includes the at least one salient region, predicting, by a trained machine learning system, at least one continuous value corresponding to the at least one salient region, and outputting the at least one continuous value to an electronic storage device and/or display.

Abstract: Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.