Apparatus for diagnosing breast cancer, the apparatus comprising a controller having a set of instructions executable to: acquire a contrast enhanced region of interest (CE-ROI) in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; determine a texture neighborhood for each of a plurality of X-ray pixels in the CE-ROI, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel; generate a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI that are located within the texture neighborhood; and use a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant.

Neural stimulation is provided according to a closed loop algorithm to treat sleep disordered breathing (SOB), including obstructive sleep apnea (OSA). The closed loop algorithm is executed by a system comprising a processor (which can be within the neural stimulator). The closed loop algorithm includes monitoring physiological data (e.g., EMG data) recorded by a sensor implanted adjacent to an anterior lingual muscle; identifying a trigger within the physiological data, wherein the trigger is identified as a biomarker for a condition related to sleep (e.g., inspiration); and applying a rule-based classification (which can learn) to the trigger to determine whether one or more parameters of a stimulation should be altered based on the biomarker.

A training data generation device includes a computer, and a computer-readable storage medium. The computer is configured to: receive an input of an annotation for second image data obtained by imaging an observation target; reflect a result of the annotation in first image data that is related to the same observation target as the observation target of the second image data, the first image data having a different at least one of imaging mode and display mode from the second image data; and generate training data for creating an inference model by using the first image data and the result of the annotation reflected in the first image data, the first image data including image data of a plurality of images, and the second image data being image data of an image obtained by combining the plurality of images included in the first image data.

A training data generation device includes a computer, and a computer-readable storage medium. The computer is configured to: receive an input of an annotation for second image data obtained by imaging an observation target; reflect a result of the annotation in first image data that is related to the same observation target as the observation target of the second image data, the first image data having a different at least one of imaging mode and display mode from the second image data; and generate training data for creating an inference model by using the first image data and the result of the annotation reflected in the first image data, the first image data including image data of a plurality of images, and the second image data being image data of an image obtained by combining the plurality of images included in the first image data.

A system and method of landmark detection using deep neural network with multi-frequency self-attention is provided. The system includes an encoder network that receives an image of an object of interest as an input and generates multi-frequency feature maps as output. The system further includes an attention layer that receives the generated multi-frequency feature maps and refines the generated multi-frequency feature maps based on correlations or associations between the received multi-frequency feature maps. The system further includes a decoder network that receives the refined multi-frequency feature maps as a second input from the attention layer and generates a landmark detection result based on the second input. The landmark detection result includes a heatmap image of the object of interest and the heatmap image indicates locations of landmark points on the object of interest in the image.

A system and method of landmark detection using deep neural network with multi-frequency self-attention is provided. The system includes an encoder network that receives an image of an object of interest as an input and generates multi-frequency feature maps as output. The system further includes an attention layer that receives the generated multi-frequency feature maps and refines the generated multi-frequency feature maps based on correlations or associations between the received multi-frequency feature maps. The system further includes a decoder network that receives the refined multi-frequency feature maps as a second input from the attention layer and generates a landmark detection result based on the second input. The landmark detection result includes a heatmap image of the object of interest and the heatmap image indicates locations of landmark points on the object of interest in the image.

An information processing apparatus includes a setting unit configured to set an imaging condition under which an imaging apparatus captures a video, a region determination unit configured to determine a detectable region in which a detection target is detectable in the video, based on the imaging condition, an acquisition unit configured to acquire a desired detection condition under which a user desires detection for the detection target to be executed, and a condition determination unit configured to determine a detection condition under which the detection target is detected from the video, based on the desired detection condition and the detectable region determined based on at least one imaging condition.

A memory device is provided including instructions that, when executed, cause one or more processors to perform the steps including receiving a plurality of images acquired by a camera, the plurality of images including a plurality of optical patterns, wherein an optical pattern of the plurality of optical patterns encodes an object identifier. The steps include presenting the plurality of images comprising the plurality of optical patterns on a display, and presenting a plurality of visual indications overlying the plurality of optical patterns in the plurality of images. The steps also include identifying a selected optical pattern of the plurality of optical patterns based on a user action and a position of the selected optical pattern in one or more of the plurality of images. The steps also include decoding the selected optical pattern to generate the object identifier and storing the object identifier in a second memory device.

A memory device is provided including instructions that, when executed, cause one or more processors to perform the steps including receiving a plurality of images acquired by a camera, the plurality of images including a plurality of optical patterns, wherein an optical pattern of the plurality of optical patterns encodes an object identifier. The steps include presenting the plurality of images comprising the plurality of optical patterns on a display, and presenting a plurality of visual indications overlying the plurality of optical patterns in the plurality of images. The steps also include identifying a selected optical pattern of the plurality of optical patterns based on a user action and a position of the selected optical pattern in one or more of the plurality of images. The steps also include decoding the selected optical pattern to generate the object identifier and storing the object identifier in a second memory device.

A system capable of determining which recognition algorithms should be applied to regions of interest within digital representations is presented. A preprocessing module utilizes one or more feature identification algorithms to determine regions of interest based on feature density. The preprocessing modules leverages the feature density signature for each region to determine which of a plurality of diverse recognition modules should operate on the region of interest. A specific embodiment that focuses on structured documents is also presented. Further, the disclosed approach can be enhanced by addition of an object classifier that classifies types of objects found in the regions of interest.