Abstract: Techniques for obtaining a synthetic histochemically stained image from a multiplexed immunofluorescence (MPX) image may include producing an N-channel input image that is based on information from each of M channels of an MPX image of a tissue section, where M and N are positive integers and N is less than or equal to M; and generating a synthetic image by processing the N-channel input image using a generator network, the generator network having been trained using a training data set that includes a plurality of pairs of images. The synthetic image depicts a tissue section stained with at least one histochemical stain. Each pair of images of the plurality of pairs of images includes an N-channel image, produced from an MPX image of a first section of a tissue, and an image of a second section of the tissue stained with the at least one histochemical stain.

Abstract: Embodiments disclosed herein generally relate to identifying auto-fluorescent artifacts in a multiplexed immunofluorescent image. Particularly, aspects of the present disclosure are directed to accessing a multiplexed immunofluorescent image of a slice of specimen, wherein the multiplexed immunofluorescent image comprises one or more auto-fluorescent artifacts, processing the multiplexed immunofluorescent image using a machine-learning model, wherein an output of the processing corresponds to a prediction that the multiplexed immunofluorescent image includes one or more auto-fluorescent artifacts at one or more particular portions of the multiplexed immunofluorescent image, adjusting subsequent image processing based on the prediction, performing the subsequent image processing, and outputting a result of the subsequent image processing, wherein the result corresponds to a predicted characterization of the specimen.

Abstract: A MEMS resonator includes a pantograph that is a parallelogram, an oscillator connected to each vertex of the pantograph, and an electrode disposed opposite each oscillator, and forming a capacitor with the oscillator. A set of the electrodes disposed opposite to a set of the oscillators along an extension direction of a diagonal line of the pantograph that is the parallelogram have applied thereto a voltage differing in phase by 180° from another set of the electrodes disposed opposite to another set of the oscillators along an extension direction of another diagonal line of the pantograph. At least two of the MEMS resonators are connected so as to share one oscillator.

Abstract: The present disclosure relates to domain swap by accessing an image from a first domain and is processed using a machine learning model to generate a virtual synthetic image in a second domain. This approach can eliminate or reduce the need to separately collect an image in the second domain, which can save time and cost. Leveraging tools that are available in the second domain to perform image processing on the virtual synthetic image. Results or analysis from the image processing in the second domain can then be directly applied to the first domain and to assess the image further. Since spatial reference points (size, scale, view etc.) are same, pixels identifying a boundary of a region depicted in the virtual synthetic image are the same pixels in the first domain.

Abstract: The present disclosure relates to stain unmixing of digital pathology images by determining initial color vectors associated with digital pathology stains (or chromogens) from pure-color digital pathology images. The determined color vectors may be fine-tuned or adjusted to help improve the stain unmixing performance. The adjustment may be performed via the interface and/or automated technique that, based on a real multiplex image and one or more synthetic singleplex images, perform adjustments to the color vectors. These adjusted color vectors may be further leveraged for stain unmixing of a given multiplex image. Additionally, the disclosure provides techniques to generate synthetic pixels and the associated color vectors, a recommended stain to be added to a multiplex image and/or generation of multiplex images from one or more digital pathology images based on the targeted color vectors.

Abstract: The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.

Abstract: Embodiments disclosed herein generally relate to expression-level prediction for digital pathology images.

Abstract: The present invention discloses an outer-rotor brushless motor and a housing thereof. The housing is applicable to an outer-rotor brushless motor. The outer-rotor brushless motor includes a spindle, a stator holder mounted on a lower portion of the spindle through a bearing, and an outer rotor assembly fixedly mounted on an upper portion of the spindle. The housing comprises a cavity with a bottom being open, and covers the outer rotor assembly. At least one platform extends outwards from a bottom of a side wall of the housing, and is detachably fixed to the stator holder to form air inlet gaps. Multiple air outlet openings corresponding to the outer rotor assembly are formed in the side wall of the housing in a circumferential direction. The motor can be effectively protected and cooled.

Abstract: The application relates a brushless motor and a rotor thereof. The rotor of the brushless motor comprises a rotor core, multiple first magnets for generating first magnetic fields in a radial direction of the rotor core, and multiple second magnets for generating second magnetic fields in a tangential direction of the rotor core; and the multiple first magnets and the multiple second magnets are alternately arranged one by one in a circumferential direction of the rotor core, so that the first magnetic fields and the second magnetic fields are mixed to form a hybrid magnetic field. The rotor can reduce electromagnetic noise of the motor and provides a counter electromotive force with better sinusoidal degree, which can satisfy requirements of FOC drive, reduce the cost of the motor, improve the performance of the motor, and reduce the weight of the motor.

Abstract: The present disclosure relates to techniques for pre-processing training data, augmenting training data, and using synthetic training data to effectively train a machine learning model to (i) reject adversarial example images, and (ii) detect, characterize and/or classify some or all regions of images that do not include adversarial example regions. Particularly, aspects of the present disclosure are directed to receiving a training set of images for training a machine learning algorithm to detect, characterize, classify, or a combination thereof some or all regions or objects within the images, augmenting the training set of images with synthetic images generated from one or more adversarial algorithms to generate augmented batches of images, and train the machine learning algorithm using the augmented batches of images to generate a machine learning model configured to detect, characterize, classify, or a combination thereof some or all regions or objects within new images.

Abstract: Duplex immunohistochemistry (IHC) staining of tissue sections allows simultaneous detection of two biomarkers and their co-expression at the single-cell level, and does not require two IHC stains and additional registration to identify co-localization. Duplex IHC are often difficult for human including pathologists to reliably score. The methods and system herein use machine-learning models and probability maps to detect and record individual phenotype ER/PR.

Abstract: Techniques for obtaining a synthetic histochemically stained image from a multiplexed immunofluorescence (MPX) image may include producing an N-channel input image that is based on information from each of M channels of an MPX image of a tissue section, where M and N are positive integers and N is less than or equal to M; and generating a synthetic image by processing the N-channel input image using a generator network, the generator network having been trained using a training data set that includes a plurality of pairs of images. The synthetic image depicts a tissue section stained with at least one histochemical stain. Each pair of images of the plurality of pairs of images includes an N-channel image, produced from an MPX image of a first section of a tissue, and an image of a second section of the tissue stained with the at least one histochemical stain.

Abstract: Disclosed herein are systems and methods of estimating the autofluorescence (AF) signal and other non-target signals in each channel of a multi-channel image of a biological sample that is stained with one or more fluorescent labels. In some embodiments, the estimated autofluorescence signal can then be subtracted or masked from the multi-channel image. In some embodiments, the autofluorescence-removed multichannel image can then be use for further processing (e.g. image analysis, etc.).

Abstract: A method for analyzing an image of a tissue section may include obtaining a plurality of image locations, each corresponding to a different one of a plurality of biological structures; obtaining a plurality of locations of a first biomarker in the image; and calculating a distance transform array for at least a portion of the image that includes the plurality of seed locations. The method may include, for each of the plurality of seed locations and based on information from the first distance transform array, detecting whether the first biomarker is expressed at the seed location, and storing, to a data structure associated with the seed location, an indication of whether expression of the first biomarker at the seed location was detected. The method may include detecting, based on the stored indications, co-localization of at least two phenotypes in at least a portion of the tissue section.

Abstract: The present disclosure relates to techniques for obtaining a synthetic immunohistochemistry (IHC) image from a histochemically stained image. Particularly, aspects of the present disclosure are directed to accessing an input image that depicts a tissue section that has been stained with at least one histochemical stain; generating a synthetic image by processing the input image using a trained generator network; and outputting the synthetic image. The synthetic image depicts a tissue section that has been stained with at least one IHC stain that targets a first antigen, and techniques may also include receiving an input that is based on a level of expression of a first antigen from the synthetic image and/or generating, from the synthetic image, a value that is based on a level of expression of the first antigen.

Abstract: A smart switch system comprising one or more switching devices. Each one of the switching devices include a first pin, a second pin, a current indication pin, a system current limit pin and a power switch for electrically coupling the first pin to the second pin when the power switch is turned on. Each switching device may adaptively adjust an operation current limit value of the switching device based on a system total current limit value received or set at the system current limit pin and a system current indication signal received at the current indication pin.

Abstract: A switching converter with adaptive constant on-time control has a power switch and a control circuit. The switching converter converts an input voltage into an output voltage. When the switching converter is in a light-load state, the on-time of the power switch is controlled to be smaller than the on-time of the power switch in a normal-load state, wherein the normal-load state includes a continuous current mode (CCM) or a discontinuous current mode (DCM).

Abstract: Embodiments disclosed herein generally relate to identifying auto-fluorescent artifacts in a multiplexed immunofluorescent image. Particularly, aspects of the present disclosure are directed to accessing a multiplexed immunofluorescent image of a slice of specimen, wherein the multiplexed immunofluorescent image comprises one or more auto-fluorescent artifacts, processing the multiplexed immunofluorescent image using a machine-learning model, wherein an output of the processing corresponds to a prediction that the multiplexed immunofluorescent image includes one or more auto-fluorescent artifacts at one or more particular portions of the multiplexed immunofluorescent image, adjusting subsequent image processing based on the prediction, performing the subsequent image processing, and outputting a result of the subsequent image processing, wherein the result corresponds to a predicted characterization of the specimen.

Abstract: The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.

Abstract: A multiplex image is accessed that depicts a particular slice of a particular sample stained with two or more dyes. Using a Generator network, a predicted singleplex image is generated that depicts the particular slice of the particular sample stained with each of the expressing biomarkers. The Generator network may have been trained by training a machine-learning model using a set of training multiplex images and a set of training singleplex images. Each of the set of training multiplex images depicted a slice of a sample stained with two or more dyes. Each of the set of training singleplex images depicted a slice of a sample stained with a single dye. The machine-learning model included a Discriminator network configured to discriminate whether a given image was generated by the Generator network or was a singleplex image of a real slide. The method further includes outputs the predicted singleplex image.