Abstract: Systems and methods are disclosed for generating a specialized machine learning model by receiving a generalized machine learning model generated by processing a plurality of first training images to predict at least one cancer characteristic, receiving a plurality of second training images, the first training images and the second training images include images of tissue specimens and/or images algorithmically generated to replicate tissue specimens, receiving a plurality of target specialized attributes related to a respective second training image of the plurality of second training images, generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of second training images and the target specialized attributes, receiving a target image corresponding to a target specimen, applying the specialized machine learning model to the target image to determine at least one characteristic of the target image, and outputting the characteristic of the tar

Abstract: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, detecting one or more image regions from a background of the one or more digital images, determining a prediction, using a machine learning system, of whether at least one first image region of the one or more image regions comprises at least one external contaminant, the machine learning system having been trained using a plurality of training images to predict a presence of external contaminants and/or a location of any external contaminants present in the tissue specimen, and determining, based on the prediction of whether a first image region comprises an external contaminant, whether to process the image region using an processing algorithm.

Abstract: The present application relates generally to image tiling, including but not limited to systems and methods of fast whole slide tissue tiling. A computing system may identify a first dimension of a first image from which to select one or more tiles. The computing system may perform a reduction operation on the first image to generate a second dimension of a second image. The computing system may perform a smoothening operation on the second image. The computer system may identify a first set of pixels in the second image corresponding to a presence of a feature and a second set of pixels corresponding to an absence of the feature. The computing system may select, from a plurality of tiles corresponding to the first image, a subset of tiles corresponding to the first set of pixels identified from the second image.

Abstract: Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to identify a region of interest of the target specimen and determine an expression level of, category of, and/or presence of a biomarker in the region of interest, the biomarker comprising at least one from among an epithelial growth factor receptor (EGFR) biomarker and/or a DNA mismatch repair (MMR) deficiency biomarker, the machine learning system having been generated by processing a plurality of training images to predict whether a region of interest is present in the target electronic image, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the determined expression level of, category of, and/or presence of the biomarker in the region of interest.

Abstract: Described herein are Deep Multi-Magnification Networks (DMMNs). The multi-class tissue segmentation architecture processes a set of patches from multiple magnifications to make more accurate predictions. For the supervised training, partial annotations may be used to reduce the burden of annotators. The segmentation architecture with multi-encoder, multi-decoder, and multi-concatenation outperforms other segmentation architectures on breast datasets, and can be used to facilitate pathologists' assessments of breast cancer in margin specimens.

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.

Abstract: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, detecting one or more image regions from a background of the one or more digital images, determining a prediction, using a machine learning system, of whether at least one first image region of the one or more image regions comprises at least one external contaminant, the machine learning system having been trained using a plurality of training images to predict a presence of external contaminants and/or a location of any external contaminants present in the tissue specimen, and determining, based on the prediction of whether a first image region comprises an external contaminant, whether to process the image region using an processing algorithm.

Abstract: Systems and methods are disclosed for generating a specialized machine learning model by receiving a generalized machine learning model generated by processing a plurality of first training images to predict at least one cancer characteristic, receiving a plurality of second training images, the first training images and the second training images include images of tissue specimens and/or images algorithmically generated to replicate tissue specimens, receiving a plurality of target specialized attributes related to a respective second training image of the plurality of second training images, generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of second training images and the target specialized attributes, receiving a target image corresponding to a target specimen, applying the specialized machine learning model to the target image to determine at least one characteristic of the target image, and outputting the characteristic of the tar

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 digital pathology images, prioritizing the digital pathology images, and outputting a sequence of the digital pathology images based on the prioritization. The prioritization may be determined by a machine learning model trained to determine prioritization values based on various criteria. For example, the machine learning may generate biomarker expression information and determine prioritization values based on the generated information.

Abstract: The present application relates generally to image tiling, including but not limited to systems and methods of fast whole slide tissue tiling. A computing system may identify a first image of a first dimension from which to select one or more tiles. The computing system may perform a reduction operation on the first image to generate a second image of a second dimension. The computing system may apply a thresholding operation on the second image to identify a first set of pixels corresponding to the presence of the feature and a second set of pixels corresponding to the absence of the feature based on an intensity of each pixel in the second image. The computing system may select, from a plurality of tiles corresponding to the first image, a subset of tiles corresponding to the first set of pixels identified from the second image.

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: 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: 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: 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.