Abstract: A clinical trial re-evaluation system is operable to perform at least one assessment function on a set of medical scans for each of a first subset of a set of patients of a failed clinical trial to generate automated assessment data for each of the first subset of the set of patients. The first subset of the set of patients corresponds to a subset of human assessment data determined to have failed to meet criteria of the clinical trial. Patient re-evaluation data is generated for each of the first subset of the set of patients by comparing the automated assessment data to the criteria. The patient re-evaluation data for a second subset of the first subset of the set of patients indicates the automated assessment data passes the criteria. Trial re-evaluation data is generated based on the patient re-evaluation data for transmission to a computing device for display.

Abstract: A curable composition includes a first part comprising an epoxy resin; and a second part comprising a multifunctional, functional thiol containing compound. The curable composition further includes an inorganic filler present in an amount of at least 20 weight %, based on the total weight of the curable composition. The multifunctional, functional thiol containing compound comprises an ether in the backbone thereof.

Abstract: Methods and related systems for filling a gap feature comprised in a substrate are disclosed. The methods comprise a step of providing a substrate comprising one or more gap features into a reaction chamber. The one or more gap features comprise an upper part comprising an upper surface and a lower part comprising a lower surface. The methods further comprise a step of subjecting the substrate to a first plasma treatment and subjecting the substrate to a second plasma treatment. Thus the upper surface is inhibited while leaving the lower surface substantially unaffected. Then, the methods comprise a step of selectively depositing a material on the lower surface.

Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.

Abstract: Methods and related systems for filling a gap feature comprised in a substrate are disclosed. The methods comprise a step of providing a substrate comprising one or more gap features into a reaction chamber. The one or more gap features comprise an upper part comprising an upper surface and a lower part comprising a lower surface. The methods further comprise a step of subjecting the substrate to a plasma treatment. Thus, the upper surface is inhibited while leaving the lower surface substantially unaffected. Then, the methods comprise a step of selectively depositing a silicon-containing material on the lower surface.

Abstract: A curable composition includes a first part comprising an epoxy resin; and a second part comprising a multifunctional, functional thiol containing compound. The curable composition further includes an inorganic filler present in an amount of at least 40 weight %, based on the total weight of the curable composition. The multifunctional, functional thiol containing compound comprises ether in the backbone thereof.

Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of medical labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the medical labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Preliminary heat map visualization data can be generated for transmission to a client device based on the probability matrix data. Heat map visualization data can be generated via a post-processing of the preliminary heat map visualization data to mitigate heat map artifacts.

Abstract: A multi-model medical scan analysis system is operable to generate a plurality of training sets from a plurality of medical scans. Each of a set of sub-models can be generated by performing a training step on a corresponding one of the plurality of training sets. A subset of the set of sub-models is selected for a new medical scan. A set of abnormality data is generated by applying a subset of a set of inference functions on the new medical scan, where the subset of the set of inference functions utilize the subset of the set of sub-models. Final abnormality data is generated by performing a final inference function on the set of abnormality data. The final abnormality data can be to a client device for display via a display device.

Abstract: A lesion tracking system is operable to receive a first medical scan and second medical scan associated with a patient ID. A lesion area calculation is performed on a first subset of image slices determined to include a lesion detected in the first medical to generate a first set of lesion area measurements. The lesion area calculation is performed on a second subset of image slices determined to include the lesion in the second medical scan to generate a second set of lesion area measurements. A lesion volume calculation is performed on the first set of lesion area measurements and the second set of lesion area measurements to generate a first lesion volume measurement and a second lesion volume measurement, respectively, and the first and second lesion volume measurements are utilized to calculate a lesion volume change for transmission to a client device for display via a display device.

Abstract: A contrast parameter learning system is operable to generate contrast significance data for a computer vision model, where the computer vision model was generated by performing a training step on a training set of medical scans. Significant contrast parameters are identified based on the contrast significance data. A re-contrasted training set is generated by performing an intensity transformation function that utilizes the significant contrast parameters on the training set of medical scans. A re-trained model is generated by performing the training step on the first re-contrasted training set. Re-contrasted image data of a new medical scan is generated by performing the intensity transformation function. Inference data is generated by performing an inference function that utilizes the first re-trained model on the re-contrasted image data. The inference data is transmitted via the transmitter to a client device for display via a display device.

Abstract: A longitudinal data quality assurance system is operable to receive a set of medical scans corresponding to a same first patient. A first chronologically ordered list of the set of medical scans is generated based on a corresponding first set of dates, where each of the corresponding first set of dates are extracted from a headers of the set of medical scans. Quality assurance data is generated for the first chronologically ordered list by performing at least one quality assurance function on at least one of the set of medical scans. A second chronologically ordered list that includes a first subset of the first set of medical scans is generated to rectify at least one continuity error of the first chronologically ordered list, indicated in the quality assurance data. The second chronologically ordered list is transmitted to a client device for display via a display device.

Abstract: A method for dynamically configuring as required the layout and number of parking spaces for various types of vehicles includes acquiring types of vehicles entering a parking lot, the parking lot comprising at least two types of parking spaces, and determining whether available parking spaces as currently laid out match the types and numbers of types of vehicles entering the parking lot. Available parking spaces can be provided or rearranged to match the vehicles entering the parking lot if there is a sufficiency of space to accommodate and match the plurality of vehicles to enter the parking lot.

Abstract: The disclosure provides electrically conductive elastomers.

Abstract: A medical scan header standardization system is operable to determine a set of standard DICOM headers based on determining a standard set of fields and based on further determining a standard set of entries for each of the standard set of fields. A DICOM image is received via a network, and a header of the DICOM image is determined to be incorrect. A selected one of the set of standard DICOM headers to replace the header of the DICOM image is determined. The selected one of the set of standard DICOM headers is transmitted, via the network, to a medical scan database for storage in conjunction with the DICOM image.

Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.

Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of medical labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the medical labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Preliminary heat map visualization data can be generated for transmission to a client device based on the probability matrix data. Heat map visualization data can be generated via a post-processing of the preliminary heat map visualization data to mitigate heat map artifacts.

Abstract: A medical scan triaging system is operable to train a computer vision model and to generate abnormality data indicating abnormality probabilities for medical scans via the computer vision model. A first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a first probability value of a triage probability threshold. A second subset of medical scans is determined by identifying medical scans with abnormality probabilities less than the first probability value. An updated first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a second probability value of an updated triage probability threshold. An updated second subset of the plurality of medical scans is determined by identifying medical scans with a abnormality probabilities less than the second probability value. The updated first subset of medical scans is transmitted to client devices.

Abstract: A medical scan interface feature evaluator system is operable to receive a set of responses from each of a set of client devices, where each set of responses is generated based on a corresponding client device displaying each of the set of medical scans in conjunction with at least one interface feature indicated in an image-to-prompt mapping. Response score data is generated for each response of the set of responses received from each of the set of client devices by comparing each response to truth annotation data of a corresponding medical scan of the set of medical scans indicated by the image-to-prompt mapping. Interface feature score data corresponding to each user interface feature in the set of user interface features is generated based on aggregating corresponding response score data. A ranking of the set of user interface features is generated based on the interface feature score data.

Abstract: An electronic component and a method of manufacturing an electronic component, the method including surface mounting electronic components to a printed circuit board (PCB), applying a flip-chip die integrated circuit (IC) to the PCB and underfilling the flip-chip IC to secure the PCB. The method also includes sintering a copper block to the PCB, where the copper block is in thermal communication with the IC and acts as a thermal path for removing heat generated by the flip-chip IC.

Abstract: A multi-model medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans and corresponding labeling data. A plurality of fine-tuned models are generated by performing a fine-tuning step on the generic model. Abnormality detection data is generated for a new medical scan by utilizing the generic model. A first one of the plurality of abnormality types that is detected in the new medical scan is determined based on a corresponding one of the plurality of probability values. Additional abnormality data is generated by performing a fine-tuned inference function on the image data of the new medical scan that utilizes one of the plurality of fine-tuned models that corresponds to the first one of the plurality of abnormality types. The additional abnormality data is transmitted for display.