Abstract: A method includes receiving intraoral scan data of an intraoral cavity of a patient; processing the intraoral scan data to determine, for each dental condition of a plurality of dental conditions, whether the dental condition is detected for the patient and a severity of the dental condition; and presenting indications of the plurality of dental conditions together in a graphical user interface (GUI), wherein the indications show, for each dental condition of the plurality of dental conditions, whether the dental condition was detected for the patient and the severity of the dental condition.

Abstract: A medical information processing apparatus according to an embodiment includes a processing circuitry. The processing circuitry is configured to: obtain first nerve activity information indicating an activity of a nerve of a subject; estimate first feeling information indicating a feeling experienced by the subject on the basis of the first nerve activity information; and determine presentation information to enable an evaluator to experience the feeling of the subject, on the basis of the first feeling information.

Abstract: Detection of synthetic content in portrait videos, e.g., deep fakes, is achieved. Detectors blindly utilizing deep learning are not effective in catching fake content, as generative models produce realistic results. However, biological signals hidden in portrait videos which are neither spatially nor temporally preserved in fake content, can be used as implicit descriptors of authenticity. 99.39% accuracy in pairwise separation is achieved. A generalized classifier for fake content is formulated by analyzing signal transformations and corresponding feature sets. Signal maps are generated, and a CNN employed to improve the classifier for detecting synthetic content. Evaluation on several datasets produced superior detection rates against baselines, independent of the source generator, or properties of available fake content.

Abstract: A medical image processing apparatus includes a recognition unit that detects a region of interest from a medical image and classifies the region of interest, a display control unit that causes emphasis display of the region of interest and a classification for the region of interest to be displayed in a screen for displaying the observation target, and a change determining unit that determines at least any of whether to change a display style of the emphasis display or whether to change a display style of the classification. The display control unit changes the display style of the emphasis display in the case where it is determined to change the display style of the emphasis display and changes the display style of the classification in the case where it is determined to change the display style of the classification.

Abstract: An image processing apparatus comprises a first obtaining unit that obtains examination information, a readout unit that reads out learning result data selected based on the examination information from a storage unit storing at least one piece of learning result data that has been obtained by machine learning in advance, a second obtaining unit that obtains a medical image obtained based on the examination information obtained by the first obtaining unit, and a processing unit that processes the medical image obtained by the second obtaining unit using learning result data read out by the readout unit.

Abstract: A method for assisting in cell isolation from a biological tissue section includes: obtaining cell images that are each an image of cells isolated from the biological tissue section, the biological tissue section being soaked in a solution containing an enzyme; calculating, from the cell images, a number of the cells isolated from the biological tissue section as an indicator of the cell isolation from the biological tissue section; and visualizing a temporal change in the indicator on the basis of a history of the indicator.

Abstract: Method and system for compensating intensity biases in a plurality of digital images. Each digital image of the plurality of digital images contains a plurality of objects and each of the plurality of objects is configured to receive at least one molecule comprising genetic information, wherein the at least one molecule is configured to receive one of at least a first fluorescent compound and a second fluorescent compound. A first digital image of the plurality of digital images is taken by an optical imaging system during emission of electromagnetic radiation by the first fluorescent compound, and a second digital image of the plurality of digital images is taken by the optical imaging system during emission of electromagnetic radiation by the second fluorescent compound.

Abstract: Based on deep neural net and possibly on an active shape model approach, the complete outline of an anatomy may be determined and classified. Based on certain features, an algorithm may assess whether the viewing angle of the C-arm is sufficient or not. In a further step, an algorithm may estimate how far away from the desired viewing angle the current viewing angle is and may provide guidance on how to adjust the c-arm position to reach the desired viewing angle.

Abstract: Among other things, the techniques described herein include a method for receiving a plurality of images of one or more containers while the one or more containers are being emptied, the plurality of images comprising a training set of images and a validation set of images; labeling each image of the plurality of images as including either an overfilled container or a not-overfilled container; processing each image of the plurality of images to reduce bias of a machine learning model; training, and based on the labeling, the machine learning model using the plurality of images; and optimizing the machine learning model by performing learning against the validation set, the optimized machine learning model being used to generate a prediction for a new image of a container, the prediction indicating whether the container in the new image was overfilled prior to the new container being emptied.

Abstract: A camera apparatus including a central processing unit configured to capture raw image sensor data of a field-of-view of an agricultural field, concurrently execute a plurality of different image transformation operations in a single pass on the captured raw image sensor data to obtain a processed image output, based on an one-time read of pixel values of the captured raw image sensor data and push the processed image output in a shared memory accessible to a plurality of application nodes in the camera apparatus. The camera apparatus includes a graphical processing unit configured to execute a first neural network model on the processed image output to detect one or more foliage regions in the processed image output and concomitantly execute a second neural network model on the processed image output to detect one or more crop plants in the processed image output.

Abstract: One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

Abstract: A method for characterising a perivascular region using medical imaging data of a subject. The method comprises calculating the value of a radiomic signature of the perivascular region using the medical imaging data. Also disclosed is a method for deriving a radiomic signature for predicting cardiovascular risk. The method comprises obtaining a radiomic dataset and using the radiomic dataset to construct a perivascular radiomic signature. Also disclosed are systems for performing the aforementioned methods.

Abstract: A method of determining a roadway map includes receiving an image from above a roadway. The method further includes generating a skeletonized map based on the received image, wherein the skeletonized map comprises a plurality of roads. The method includes identifying intersections based on joining of multiple roads of the plurality of roads in the skeletonized map. The method includes partitioning the skeletonized map based on the identified intersections, wherein partitioning the skeletonized map defines a roadway data set and an intersection data set. The method includes analyzing the roadway data set to determine a number of lanes in each roadway of the plurality of roads. The method further includes analyzing the intersection data set to lane connections in the identified intersections. The method further includes merging results of the analyzed road data set and the analyzed intersection data set to generate the roadway map.

Abstract: An anomaly detection apparatus extracts a circumstantial feature value for anomaly detection corresponding to a circumstantial feature value for learning from other modal signal for anomaly detection different in modal from acoustic, calculates a signal pattern feature related to an acoustic signal of anomaly detection target based on the acoustic signal of anomaly detection target, the circumstantial feature value for anomaly detection and a signal pattern model learned based on an acoustic signal for learning and the circumstantial feature value for learning calculated from other modal signal for learning, and calculates an anomaly score for performing an anomaly detection of the acoustic signal of anomaly detection target based on the signal pattern feature.

Abstract: A system and method generating an optimized medical image using a machine learning model are provided. The method includes (i) receiving one or more medical images, (ii) segmenting to generate a transformed medical image for detecting a plurality of target elements, (iii) displaying the transformed medical image, (iv) receiving markings and scribblings associated with scribble locations from a user, (v) identifying errors associated with an outline of a target element, (vi) computing a loss function for a location of pixels where the target element is located on the transformed medical image, (vii) modifying the pre-defined weights (w) to match the segmentation output and the determined target element, (viii) determining whether the segmentation output is matched with the target element and (ix) generating the optimized medical image if the segmentation output is matched with the determined target element.

Abstract: A system to generate images based on imaging data of a portion of a body and physiological event data associated with a physiological process of the body.

Abstract: To obtain feedback on image quality from qualified reviewers, an optically machine readable code (124) (e.g., a QR code or the like) is generated for each acquired medical image and embedded into the image. The embedded code includes information to the identity of the image, the imaging device, authorized reviewers, and authorized recipients of the feedback, as well as a link to a feedback form that can be retrieved by a communication device (38) used by an authorized user. When the embedded code is scanned by the communication device, the code is decoded and the feedback form is retrieved from a server, completed by the reviewer, and transmitted back to the authorized recipients of the feedback.

Abstract: Embodiments of the disclosure provide methods and systems for joint abnormality detection and physiological condition estimation from a medical image. The exemplary method may include receiving, by at least one processor, the medical image acquired by an image acquisition device. The medical image includes an anatomical structure. The method may further include applying, by the at least one processor, a joint learning model to determine an abnormality condition and a physiological parameter of the anatomical structure jointly based on the medical image. The joint learning model satisfies a predetermined constraint relationship between the abnormality condition and the physiological parameter.

Abstract: There is provided a cell evaluation device comprising an acquisition unit that acquires an image set of a plurality of cell images in which a plurality of cells having a protrusive structure are imaged in time series during culture; a reception unit that receives designation of a cell of interest which is a cell of interest to a user, the cell being at least one cell among the plurality of cells which are shown in one cell image of interest other than an oldest cell image in the image set; a specifying unit that captures a growth process of the protrusive structure based on the image set to specify a protrusive structure of interest, which is the protrusive structure extending from the cell of interest in the cell image of interest; and a display control unit that performs control to display the protrusive structure of interest in a display form different from that of other protrusive structures in the cell image of interest.

Abstract: The present application discloses a target detection method and device for a high-resolution remote sensing image, which comprises: acquiring an original high-resolution remote sensing image from a sensor; acquiring target information of an area and an expanded area of the area expanding around by a predetermined distance; adaptively partitioning the original high-resolution remote sensing image to obtain different cluster areas to be detected, and obtaining an area required to be detected and an area not required to be detected; selecting a model of the area required to be detected and generating a target detection scheme; executing the target detection scheme to obtain a detection result; determining whether a computing platform has extra computing resources to detect the area not required to be detected, if so, performing dynamic partition detection for the area to obtain a detection result, and merging the detection results into a target detection result.