Abstract: The present invention is related to the field of microfluidics and compound distribution within microfluidic devices and their associated systems. In one embodiment, present invention aims to solve the problem of molecule and compound absorbency into the materials making up laboratory equipment, microfluidic devices and their related infrastructure, without unduly restricting gas transport within microfluidic devices.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for the disease states based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models.

Abstract: A method (100) of analyzing a medical report (34) presenting clinical content determined from one or more images (38) includes: extracting a text embedding (54) from the medical report: extracting an image embedding (52) from the one or more images; determining one or more content feature vectors from the text embedding and the image embedding; determining one or more style feature vectors from the text embedding; and at least one of: extracting one or more clinical findings contained in the medical report using the one or more content feature vectors; scoring the style of the medical report using the one or more style feature vectors; and/or converting the medical report to a target style using the one or more content feature vectors and one or more target style feature vectors different from the determined one or more style feature vectors.

Abstract: A system and method for performing image processing. The method includes receiving an image of a first modality and a real image of a second modality, the image of the first modality and the image of the second modality capturing respective images of a same subject, applying a first trained model to the image of the first modality to generate an artificial image mimicking the image of the second modality, applying a second trained model to the artificial image mimicking the image of the second modality and data of the image of the first modality, and outputting at least one conclusion regarding the generated artificial image.

Abstract: Disclosed herein is a multi-modal sensor system, including a wearable device configured to receive signals relating to a swallowing process of a subject, the wearable device including one or more surface Electromyograph sensors configured to receive signals relating to electrical potential in muscles of the throat, one or more bio-impedance sensors, one or more memories, one or more processors configured to operate one or more sensors of the wearable device, synchronize the signals to one or more predetermined events to generate a synchronization feature, receive the signals as a first diagnostic data set, analyze the first diagnostic data set, assess, based on the analysis, the swallowing process of the subject to yield an assessment output, and present the assessment output and determine a bio-impedance signal.

Abstract: In an embodiment, a computer-implemented method (100) is described. The method (100) comprises receiving (102) input data. The method 100 further comprises generating (104) location data indicative of a location of any detected at least one feature of interest in the received input data. The location data is generated using a first machine learning, ML, model configured to detect whether or not there is at least one feature of interest in the received input data. The first ML model is trained based on a learning process implemented by a second ML model configured to detect whether or not there is at least one feature of interest in the received input data. The first ML model and the second ML model are each configured to use an attention mechanism to generate: at least one attention map from at least one layer of the first ML model; and a plurality of attention maps from a plurality of layers of the second ML model. The first ML model comprises fewer layers than the second ML model.

Abstract: A filter for a water cooler includes a housing having a surface and a cavity with cavity ports. The filter end is adapted to be received in the housing. The filter end includes a cylindrical base having a surface and a neck extending from the surface. The neck has filter ports and a side surface. The side surface has oppositely directed axially extending elongated protrusions configured to be received in channels in the housing. The base surface includes a complex set of protrusions which are adapted to be received in a similarly configured set of recesses in the housing surface. The protrusions/recesses may take the form of letters, numbers or symbols. The filter end is be fully seated in the housing in bayonet-type fashion to connect the filter ports and the housing ports o permit water to flow through the filter from the water supply to the water cooler.

Abstract: A wall mounted filtering system includes a chassis having an outwardly facing planer surface, a central recess bay and at least one of an upper recess bay and a lower recess bay. The system includes a face plate covering at least a portion of the outwardly facing planer surface and a filter assembly including at least a first stage filter oriented horizontally within the at least one bays and attached to the chassis via a first hinge, the first stage filter being operably coupled to the chassis with the first hinge and second hinge to tilt outward between an initial filter position in which the first stage filter is located within at least one of the upper recess bay and the lower recess bay below the outwardly facing planer surface, and a final filter position in which the first stage filter extends outward from the outwardly facing planer surface.

Abstract: A filter for a water cooler includes a housing having a surface and a cavity with cavity ports. The filter end is adapted to be received in the housing. The filter end includes a cylindrical base having a surface and a neck extending from the surface. The neck has filter ports and a side surface. The side surface has oppositely directed axially extending elongated protrusions configured to be received in channels in the housing. The base surface includes a complex set of protrusions which are adapted to be received in a similarly configured set of recesses in the housing surface. The protrusions/recesses may take the form of letters, numbers or symbols. The filter end is be fully seated in the housing in bayonet-type fashion to connect the filter ports and the housing ports o permit water to flow through the filter from the water supply to the water cooler.

Abstract: The present invention is related to the field of microfluidics and compound distribution within microfluidic devices and their associated systems. In one embodiment, present invention aims to solve the problem of molecule and compound absorbency into the materials making up laboratory equipment, microfluidic devices and their related infrastructure, without unduly restricting gas transport within microfluidic devices.

Abstract: A method for training a probabilistic encoder-decoder having a latent space, the method including: extracting different types of medical data for a group of individuals; creating a data matrix X including the extracted medical data, wherein each row of the data matrix X includes data for one of the group of individuals; creating condition matrix C including features to define a clinical condition, wherein each row of the condition matrix C includes the condition data for one of the group of individuals; and training the encoder and the decoder to learn the latent space by minimizing the reconstruction loss and using a regularization effect to force clinically similar inputs to be close together in the latent space.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an atrial fibrillation (AF) detection method (100). The method includes: generating a time-frequency representation of an electrocardiogram (ECG) signal acquired over a time interval; processing the time-frequency representation using a neural network (NN) (32) to output probabilities for rhythms of a set of rhythms including at least atrial fibrillation; assigning a rhythm for the ECG signal based on the probabilities for the rhythms of the set of rhythms output by the neural network; and controlling a display device (24) to display the rhythm assigned to the ECG signal.

Abstract: A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for the disease states based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models .

Abstract: A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for each disease state based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an atrial fibrillation (AF) detection method (100). The method includes: generating a time-frequency representation of an electrocardiogram (ECG) signal acquired over a time interval; processing the time-frequency representation using a neural network (NN) (32) to output probabilities for rhythms of a set of rhythms including at least atrial fibrillation; assigning a rhythm for the ECG signal based on the probabilities for the rhythms of the set of rhythms output by the neural network; and controlling a display device (24) to display the rhythm assigned to the ECG signal.

Abstract: A system and method for visualizing and annotating temporal trends of an abnormal condition in patient data. A classification and visualization module detects one or more conditions in one or more images, e.g. X-ray images, and visualizes the condition on the image. A temporal disease state extraction module analyzes text, e.g. radiology reports, for indications of a change in the condition. A multimodal disease state comparison module fuses the extracted data into a compact representation of the condition changes over time.