Abstract: A method for detection and pathological classification of polyps via colonoscopy based on an anchor-free technique includes: performing feature extraction on a color endoscopic image that is pretreated, enhancing and extending the extracted features, decoding the feature information of the enhanced feature and the extended feature through an anchor-free detection algorithm to acquire a polyp prediction box and a prospect prediction mask, then respectively extracting global and local feature vectors from the extended feature and the prospect prediction mask, and combining the global feature vector with the local feature vector, so as to predict the type of polyps through a full-connection layer. Through the present application, the type of polyps can be correctly predicted, and the detection rate of polyps and the accuracy rate of pathological classification are improved.

Abstract: A neural-network-based-implemented ophthalmologic intelligent consultation method includes: performing correction filtering on a consultation voice of a patient, framing the voice into a consultation voice frame sequence, generating a consultation text corresponding to the consultation voice frame sequence based on phoneme recognition and phoneme transcoding, and extracting an ophthalmologically-described disease; performing gray-level filtering, primary picture segmentation, and size equalization operation on an eye picture set of the to-be-diagnosed patient to acquire a standard eyeball picture group; extracting eye white features, pupil features and blood vessel features from the standard eyeball picture group, performing lesion feature analysis on the eye white features, the pupil features and the blood vessel features to acquire an ophthalmologically-observed disease, and based on the ophthalmologically-observed disease and the ophthalmologically-described disease, generating a consultation result.

Abstract: Techniques for processing multiplexed immunofluorescence (MxIF) images. The techniques include obtaining at least one MxIF image of a same tissue sample, obtaining information indicative of locations of cells in the at least one MxIF image, identifying multiple groups of cells in the at least one MxIF image at least in part by determining feature values for at least some of the cells using the at least one MxIF image and the information indicative of locations of the at least some cells in the at least one MxIF image and grouping the at least some of the cells into the multiple groups using the determined feature values, and determining at least one characteristic of the tissue sample using the multiple cell groups.

Abstract: The present disclosure provides a method, device and equipment for identifying and detecting a macular region in a fundus image. The method includes the following steps: reading a current fundus image to be positioned and detected; detecting a macular region in the fundus image using a target detection model; when the macular region in the fundus image is not detected, detecting an optic disk region in the fundus image, and identifying and positioning the macular region based on the detected optic disk region; based on a positioning result of the macular region, extracting a macular image corresponding to the macular region from the fundus image; and performing multi-modal processing on the macular image, fusing images obtained by the multi-modal processing to obtain a fused image, and detecting whether the macular region is qualified or not according to the fused image.

Abstract: Surveillance systems process video streams obtained by a surveillance device, such as a drone, to either obscure or highlight objects in a surveillance area based on tags associated with the objects. A method of providing obscurant data includes receiving image data including an image of a target and receiving a preference setting corresponding to the target. Obscurant data of at least a portion of the image data corresponding to the target are determined using the received preference setting. A method of providing surveillance image data includes capturing image data including an image of a target, querying a database to receive a preference setting corresponding to the target, determining the obscurant data of the portion of the image data, and selectively modifying the received image data according to the determined obscurant data to provide the surveillance image data.

Abstract: A method of facilitating processing of pathology images involves receiving pathology image data representing a pathology image having a plurality of image regions, wherein the pathology image data includes, for each of the plurality of image regions, a respective plurality of representations of the image region including a first representation and a second representation, the second representation having a smaller data size than the first representation. The method involves, for each of the plurality of image regions: determining, based at least in part on the first representation of the image region, a first set of image properties, determining whether the first set of image properties meets first image property criteria, and, if the first set of image properties meets the first image property criteria, producing signals for causing the second representation to be used in place of the first representation. Other methods, systems, and computer-readable media are disclosed.

Abstract: A method including correcting output of an image sensor unit based on correction information of the image sensor unit that captures and discriminates a paper sheet. A apparatus includes an image sensor unit which includes an image capturing element that captures a banknote so as to discriminate the banknote, a controller that controls the image sensor unit, and a first attachment part, to which the image sensor unit is attached to be detachable, and which includes a connection terminal that connects the image sensor unit and the controller with each other, and which is arranged in a conveyance path for conveying the banknote.

Abstract: The present embodiments relate generally to a probe, system, and method for generating a predictive model for moving the probe. The probe can include a movable element that move according to the suggestion of the predictive model. The system can include the probe, a user device, an administrator processor, and a server. The predictive model calculates an image score based on the quality, then the processor can move the movable element based on the score.

Abstract: Systems and methods for deep learning based magnetic resonance imaging (MRI) examination acceleration are provided. The method of deep learning (DL) based magnetic resonance imaging (MRI) examination acceleration comprises acquiring at least one fully sampled reference k-space data of a subject and acquiring a plurality of partial k-space of the subject. The method further comprises grafting the plurality of partial k-space with the at least one fully sampled reference k-space data to generate a grafted k-space for accelerated examination. The method further comprises training a deep learning (DL) module using the fully sampled reference k-space data and the grafted k-space to remove the grafting artifacts.

Abstract: The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.

Abstract: Disclosed is a system and a method for determining a brock score. A CT scan image may be resampled into a plurality of slices using a bilinear interpolation. A nodule may be detected on one or more of the plurality of slices. A region of interest associated with the nodule may be identified using an image processing technique. Further, a nodule segmentation may be performed to remove an area surrounding the region of interest. Subsequently, a plurality of characteristics associated with the nodule may be identified automatically using a deep learning model. Finally, a brock score for the patient may be determined based on the plurality of characteristics and demographic data of the patient.

Abstract: A system for detecting a scan irregularity in scanning process during check-out at a retail store, includes an image receiving module for receiving a video stream of a scanning zone, an image processing module for detecting visual scan intervals in image frames of the video stream, and a decision module. The decision module is configured to process each detected visual scan interval, wherein a processed visual scan interval includes a valid scan action, wherein the valid scan action is a user action performed for scanning an item. The decision module is further configured to detect a scan irregularity in the check-out process, wherein the scan irregularity occurs when an item identified for scanning in a processed visual scan interval is absent in a list of scanned items generated by the scanner during corresponding interval, and provide an alert regarding the scan irregularity at a user computing device.

Abstract: A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.

Abstract: The present invention relates to a computer implemented method for estimating lung perfusion from CT images, comprising the steps of: providing a CT image of at least a part of the lung, in particular a CT scan taken at inspiration, and more in particular a non-contrast CT scan taken at inspiration; providing the CT image to a trained computer implemented algorithm to estimate lung perfusion based on the CT image, wherein the trained computer implemented algorithm is trained by providing a set of CT images from at least a part of the lung, in particular a CT scan taken at inspiration, and more in particular a non-contrast CT scan taken at inspiration; providing perfusion information corresponding to the CT image; and training the computer implemented algorithm to learn to estimate perfusion in a CT image based on the reference perfusion information provided during training.

Abstract: An apparatus for producing a fundus image includes: a processor and a memory; an illumination component including a light source and operatively coupled to the processor; a camera including a lens and operatively coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the apparatus to: execute an automated script for capture of the fundus image; and allow for manual capture of the fundus image.

Abstract: A method and system for automatically inferring a subject's body position in a two-dimensional image produced by a medical-imaging system are disclosed. The image is labeled with a body position selected from a semantically meaningful set of candidate positions sequenced in order of their relative locations in a subject's body. A processor performs procedures that each identify a class of image features related to pixel intensity, such as a histogram of gradients, local binary patterns, or Haar-like features. A second set of procedures employs applications of a pretrained convolutional neural network that has learned to recognize features of a specific class of medical images. The results of both types of procedures are then mapped by a pretrained support-vector machine onto candidate image labels, which are mathematically combined into a single, semantically meaningful, label most likely to identify a body position of the subject shown by the image.

Abstract: A medical image processing apparatus of embodiments includes processing circuitry. The processing circuitry is configured to acquire a data and a g-factor map. The data is generated as a result of a reception by an RF coil. The processing circuitry is configured to determine strength of denoise performed on the data on the basis of the g-factor map and perform the denoise on the data.

Abstract: The present invention relates to a method for reconstructing an image and an apparatus using the same Particularly, according to the method of the present invention, when a series of first slice images of a subject are inputted in a computing device, the computing device generates, from the first slice images, second slice images having a second slice thickness different from a first thickness, which is the slice thickness of the first slice image, and provides the generated second slice images.

Abstract: An information processing apparatus for processing an image includes circuitry unit and a communication interface. The circuitry acquires identification information to identify a set of one or more objects to be imaged by an imaging device according to a predetermined imaging sequence. The communication interface transmits at least one image of the set of one or more objects that is captured by the imaging device in association with the identification information, to a management device.

Abstract: An information processing apparatus for processing an image includes circuitry unit and a communication interface. The circuitry acquires identification information to identify a set of one or more objects to be imaged by an imaging device according to a predetermined imaging sequence. The communication interface transmits at least one image of the set of one or more objects that is captured by the imaging device in association with the identification information, to a management device.