Abstract: The present disclosure relates to a video retrieval method, a method, system and device for generating a video retrieval mapping relationship, and a storage medium. The video retrieval method comprises: acquiring a retrieval instruction, wherein the retrieval instruction carries retrieval information for retrieving a target frame picture; and obtaining the target frame picture according to the retrieval information and a preset mapping relationship.

Abstract: A system for recording, storing and processing diagnostic information, including: a computer implementing a computer-readable media including digital data and ground truth; a registry constructed and arranged to store and associate transactions or accesses on the data; and a machine learning system that considers each learning step modification a microtransaction for the data used in that step and which is recorded in the transaction registry. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

Abstract: This electronic endoscope system includes an evaluation value calculation unit, an assessment unit, and a representative value determination unit. The evaluation value calculation unit is configured so as to obtain an evaluation value indicating an extent of a lesion in biological tissue in each of a plurality of images captured in a predetermined section along the depth direction of a region in an organ. The assessment unit is configured so as to assess whether the extent of the lesion in the section is changed on the basis of the degree of variation of the evaluation value. The representative value determination unit is configured so as to define a representative value of the section representing the evaluation value, in a different method when the extent of the lesion is assessed to be changed and when the extent of the lesion is assessed not to be changed.

Abstract: Disclosed is a method for training a neural network to quantify the vessel calibre of retina fundus images. The method involves receiving a plurality of fundus images; pre-processing the fundus images to normalise images features of the fundus images; and training a multi-layer neural network, the neural network comprising of a convolutional unit, multiple dense blocks alternating with transition units for down-sampling image features determined by the neural network, and a fully-connected unit, wherein each dense block comprises a series of cAdd units packed with multiple convolutions, and each transition layer comprises a convolution with pooling.

Abstract: The present disclosure is directed to processing imagery using one or more machine learning (ML) models. In particular, data describing imagery comprising a plurality of different and distinct frames can be received; and based at least in part on one or more ML models and the data describing the imagery, and for each frame of the plurality of different and distinct frames, one or more scores can be determined for the frame. Each score of the score(s) can indicate a determined measure of suitability of the frame with respect to one or more of various different and distinct uses for which the ML model(s) are configured to determine suitability of imagery.

Abstract: Disclosed here is a method of generating a dental recommendation based on image processing. Further, the method may include receiving at least one patient data comprising at least one image from at least one patient device. Further, the method may include retrieving at least one dental dataset. Further, the method may include analyzing the at least one patient data and the at least one dental dataset. Further, the method may include generating at least one landmark based on the analyzing. Further, the method may include retrieving at least one dental reference dataset. Further, the method may include processing the at least one landmark and the at least one dental reference dataset, determining at least one dental recommendation based on the processing, transmitting the at least one dental recommendation to at least one external device and storing the at least one dental recommendation.

Abstract: Example systems and methods may selection of video frames using a machine learning (ML) predictor program are disclosed. The ML predictor program may generate predicted cropping boundaries for any given input image. Training raw images associated with respective sets of training master images indicative of cropping characteristics for the training raw image may be input to the ML predictor, and the ML predictor program trained to predict cropping boundaries for raw image based on expected cropping boundaries associated training master images. At runtime, the trained ML predictor program may be applied to runtime raw images in order to generate respective sets of runtime cropping boundaries corresponding to different cropped versions of the runtime raw image. The runtime raw images may be stored with information indicative of the respective sets of runtime boundaries.

Abstract: The invention relates to a method for ink control in a printing press, wherein, during an ongoing printing process, an opaque ink (02) is printed onto a print substrate (01) in a first printing unit (06), and subsequently a transparent printing ink (03) is printed onto the opaque ink (02) in a second printing unit (07), at least one actual value of the optical density of the opaque ink (02) being ascertained by a first detection device (08) for this opaque ink (02) printed onto the print substrate (01), a film thickness of this opaque ink (02) to be applied onto the print substrate (01) being set at the relevant printing unit (06) by a control unit (11) detecting the at least one actual value of the optical density of this opaque ink (02), as a function of a previously ascertained value of the optical density of the surface of the unprinted print substrate (01), in such a way that the at least one actual value of the optical density of this opaque ink (02) detected by the first detection device (08) correspon

Abstract: Disclosed herein are systems, methods, and devices for classifying ophthalmic images according to disease type, state, and stage. The disclosed invention details systems, methods, and devices to perform the aforementioned classification based on weighted-linkage of an ensemble of machine learning models. In some parts, each model is trained on a training data set and tested on a test dataset. In other parts, the models are ranked based on classification performance, and model weights are assigned based on model rank. To classify an ophthalmic image, that image is presented to each model of the ensemble for classification, yielding a probabilistic classification score—of each model. Using the model weights, a weighted-average of the individual model-generated probabilistic scores is computed and used for the classification.

Abstract: A body fluid analysis device includes processing circuitry. The processing circuitry is configured to set one or more subregions in a region of interest in a medical image. The processing circuitry is configured to set a reference direction for each of the subregions set. The processing circuitry is configured to determine a flow direction of a body fluid for each subregion. The processing circuitry is configured to determine the state of a flow of the body fluid in the region of interest on the basis of the reference direction for each subregion set and the flow direction of the body fluid for each subregion determined.

Abstract: A method for generating a module configured to determine concentration of an analyte in a sample of a body fluid is disclosed. The method includes providing a first set of measurement data derived from images of one or more test strips indicating a color transformation in response to a body fluid containing an analyte. The images can be recorded by multiple devices with differing cameras, software and/or hardware device configurations for image recording and image data processing. A neural network model can be generated in a machine learning process applying an artificial neural network and a module configured to determine concentration of an analyte in a second sample of a body fluid can be generated. Further, the present disclosure includes a system for generating the module as well as a method and a system for determining concentration of an analyte in a sample of a bodily fluid.

Abstract: The present disclosure is directed towards systems and methods that leverage machine-learned models to decrease the rate at which abnormal sites are missed during a gastroenterological procedure. In particular, the system and methods of the present disclosure can use machine-learning techniques to determine the coverage rate achieved during a gastroenterological procedure. Measuring the coverage rate of the gastroenterological procedure can allow medical professionals to be alerted when the coverage output is deficient and thus allow an additional coverage to be achieved and as a result increase in the detection rate for abnormal sites (e.g., adenoma, polyp, lesion, tumor, etc.) during the gastroenterological procedure.

Abstract: A method includes accessing a web-based property over a network; storing a plurality of images or videos from the web-based property and associations between the plurality of images or videos and a target audience identifier responsive to the web-based property having a stored association with the target audience identifier; retrieving the plurality of images or videos from the database responsive to each of the plurality of images or videos having stored associations with the target audience identifier; executing a neural network to generate a performance score for each of the plurality of images or videos; calculating a target audience benchmark; executing the neural network to generate a first performance score for a first image or video and a second performance score for a second image or video; comparing the first performance score and the second performance score to the benchmark; and generating a record identifying the first image or video.

Abstract: Provided are a medical image processing apparatus, a medical image processing method, a program, a diagnosis supporting apparatus, and an endoscope system that emphasize a region of interest in a medical image at an appropriate emphasis degree. The above problem is solved by a medical image processing apparatus including: an emphasis processing unit that emphasizes a region of interest included in a time-series medical image at a set emphasis degree; a total-time measuring unit that measures a total time during which the region of interest is emphasized; and an emphasis-degree setting unit that sets the emphasis degree to a relatively larger value as the total time is relatively longer.

Abstract: The present document relates to audio source coding systems. In particular, the present document relates to audio source coding systems which make use of linear prediction in combination with a filterbank. A method for estimating a first sample (615) of a first subband signal in a first subband of an audio signal is described. The first subband signal of the audio signal is determined using an analysis filterbank (612) comprising a plurality of analysis filters which provide a plurality of subband signals in a plurality of subbands from the audio signal, respectively.

Abstract: A method includes storing a database comprising a plurality of pointers to web pages and identifiers of entities associated with the plurality of pointers; receiving a first request comprising a first identifier; identifying subset of the plurality of pointers from the database responsive to each pointer of the subset having a stored association with a first identification that matches the first identifier; responsive to identifying the subset of the plurality of pointers, establishing, via one or more pointers, a connection with a server hosting a set of web pages associated with the subset of the plurality of pointers; retrieving one or more images or videos from each of the set of web pages over the established connection; calculating a performance score for each of the one or more images or videos; and generating a record identifying the performance score for each of the one or more images or videos.

Abstract: A system includes a display and a processor. The processor is configured to: (i) receive a dataset including multiple data points, each data point corresponding to one or more properties of an organ of a patient, (ii) produce, based on a clustering criterion, at least a cluster including two or more of the data points, and (iii) produce and present on the display, a map of the organ and at least an object indicative of the cluster. In response to selection of the object by a user, the processor is configured to produce and present on the display, a two-dimensional (2D) table including the one or more properties of each of the clustered data points.

Abstract: In order to more accurately white balance an image, weightings can be determined for pixels of an image when computing an illuminant color value of the image and/or a scene. The weightings can be based at least in part on the Signal-to-Noise Ratio (SNR) of the pixels. The SNR may be actual SNR or SNR estimated from brightness levels of the pixels. SNR weighting (e.g., SNR adjustment) may reduce the effect of pixels with high noise on the computed illuminant color value. For example, one or more channel values of the illuminant color value can be determined based on the weightings and color values of the pixels. One or more color gain values can be determined based on the one or more channel values of the illuminant color value and used to white balance the image.

Abstract: During operation, a computer system may apply a pretrained predictive model to information for at least a subset of a plurality of individuals, and may determine levels of uncertainty in results of the pretrained predictive model for at least the subset of the plurality of individuals. Then, the computer system may dynamically adapt a at least one threshold range based at least in part on the determined levels of uncertainty and a predefined target performance of the pretrained predictive model for the plurality of individuals. Next, the computer system may perform different remedial actions for a first group of individuals in the plurality of individuals having the results where the levels of uncertainty are within the at least one threshold range and a second group of individuals in the plurality of individuals having the results where the levels of uncertainty are outside of the at least one threshold range.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a quantile neural network to input an image and output a lower quantile (LQ) prediction, a median quantile (MQ) prediction and an upper quantile (UQ) prediction corresponding to an object in the image, wherein an LQ loss, an MQ loss and a UQ loss are determined for the LQ prediction, the MQ prediction and the UQ prediction respectively and wherein the LQ loss, the MQ loss and the UQ loss are combined to form a base layer loss and output the quantile neural network.