Abstract: Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes.

Abstract: A method for enhancing low brightness regions in endoscopic video includes receiving a sequence of video frames captured during an endoscopic procedure. A brightness map is generated for each frame of the sequence of video frames. The brightness map is generated based on smoothing regional brightness in the respective frame based on regional brightness gradients. The brightness map for the respective frame includes scaling weights corresponding to pixels of the respective frame. An enhanced sequence of video frames is generated in real-time by adjusting pixel values of each frame of the sequence of video frames by scaling the pixel values of the respective frame by the scaling weights of the brightness map for the respective frame to enhance brightness of low brightness regions of the respective frame. The enhanced sequence of video frames is displayed in real-time.

Abstract: A marking inspection device 100 for transparent edible objects includes: a conveying unit 2 that conveys, along a linear conveyance path 2a, a transparent edible object C on which a marking pattern has been formed; an inspecting imaging unit 30 that captures an image of the transparent edible object C being conveyed on the conveyance path 2a, and obtains inspecting imaging data; and a control unit that determines quality of the marking pattern, on the basis of the inspecting imaging data 30. In the marking inspection device 100, the inspecting imaging unit 30 includes: a camera 31 disposed so that the imaging direction is orthogonal to the conveyance path 2a; a reflective illuminating unit 32 disposed on a same side of the conveyance path 2a as the camera 31; and a transmissive illuminating unit 33 disposed on an opposite side of the conveyance path 2a from the camera 31.

Abstract: An electronic device includes setting means configured to set a predetermined function related to control of the electronic device and capable of being performed regardless of an orientation of the electronic device, orientation detection means configured to detect the orientation of the electronic device, and control means configured to perform control such that in a case where the orientation detection means detects that the electronic device is in a first orientation, a first display is given on display means to prompt to set the predetermined function depending on a setting state of the predetermined function wherein the first display includes a display item for instructing to set the predetermined function by a user operation, and in a case where the orientation detection means detects that the electronic device is in a second orientation different from the first orientation, the first display is not given.

Abstract: In some aspects, the described systems and methods provide for a method for training a deep learning model to assess liver pathology, including accessing annotated liver pathology images associated with a group of patients in one or more randomized controlled clinical trials of nonalcoholic steatohepatitis therapy, each of the annotated liver pathology images including at least one annotation describing one or more tissue characteristic categories for a portion of the image, and training the deep learning model based on the annotated liver pathology images to predict the tissue characteristic categories, selected from a group comprising steatosis, lobular inflammation, hepatocyte ballooning, and fibrosis stage.

Abstract: Disclosed are a medical indicator measuring method and an ultrasound diagnostic device therefor. The medical indicator measuring method according to an embodiment includes the steps of: an ultrasound diagnostic device acquiring ultrasound image data, extracting characteristic points from the acquired ultrasound image data, generating figure information configured from the extracted characteristic points, determining an anatomical structure from the generated figure information, and measuring a medical indicator on the basis of the determined anatomical structure.

Abstract: According to an embodiment of the present disclosure, an image analysis method is disclosed. The image analysis method may include: receiving a body medical image; detecting one or more lesions from the received body medical image using a lesion detection model; generating anatomical location information for the one or more lesions using an anatomical analysis model, based on the received body medical image and the detection result for the one or more lesions; and generating a diagnosis result for the received body medical image, based on the anatomical location information for the one or more lesions.

Abstract: An image processing apparatus processes a kinetic image obtained by kymography of irradiating a subject with radiation to photograph a moving state of the subject. The image processing apparatus includes an acquisition unit, a generator, and an output controller. The acquisition unit acquires a plurality of frame images constituting the kinetic image. The generator generates a first image which is a synthesized still image obtained by synthesizing at least two or more frame images among the plurality of frame images. The output controller that outputs the first image to an output unit. Before generating the first image, the output controller outputs a second image which is a still image based on at least one or more frame images among the plurality of frame images and has an image quality lower than an image quality of the first image.

Abstract: A method includes collecting data related to a key performance indicator (KPI) for a cell in a mobile network. The method includes aggregating the collected data for the KPI into a plurality of groups, wherein a first group of the plurality of groups comprises values of the KPI during a time period in a first day, a second group of the plurality of groups comprises values of the KPI during the time period in a second day preceding the first day, and a third group of the plurality of groups comprises values of the KPI during the time period in a third day preceding the second day. The method includes determining whether the cell is a sleeping cell based on a comparison of the first, second and third groups. The method includes labelling the cell as sleeping in response to a determination that the cell is sleeping.

Abstract: Systems and methods for automatically masking an ultrasound image are provided. A computing system is configured to: automatically compute within the ultrasound image a set of keypoints that define an estimated shape of an ultrasound beam image within the ultrasound image; automatically select, using the set of keypoints, a beam geometry from amongst a set of known beam geometries; automatically modify the set of keypoints based on the beam geometry to generate a modified set of keypoints that defines a modified shape of the ultrasound beam image; automatically compute a binary mask configured to mask pixels around the modified shape of the ultrasound beam image using at least the modified set of keypoints and the beam geometry; and automatically compute and output a masked ultrasound image using at least the binary mask.

Abstract: A system receives a first set of points corresponding to an anatomical feature. Each point in the first set of points represents a position in a first frame. The system receives a second set of points corresponding to the anatomical feature. Each point in the second set of points represents a position in a second frame. The system identifies a first subset of the first set of points and determines a first transformation to align the first subset of the first set of points with the second set of points. The first set of points is transformed based on the first transformation. The system identifies a second subset of the first set of points and determines a second transformation to align the first and second subsets of the first set of points with the second set of points. The first set of points are transformed based on the second transformation.

Abstract: There are provided a method, apparatus, system, and computer-readable recording medium for image compression. An encoding apparatus performs domain transformation and quantization on feature map information and image information. The encoding apparatus rearranges the result of domain transformation and quantization so as to have a form advantageous to the encoding procedure and encodes the result of rearrangement, thereby generating a bitstream. A decoding apparatus receives the bitstream, decodes the received bitstream, and performs inverse transformation, dequantization, and inverse rearrangement using information transmitted through the bitstream. The result of inverse transformation, dequantization, and inverse rearrangement is used for the machine-learning task of a neural network.

Abstract: An image processing apparatus includes at least one processor, and the processor derives three-dimensional coordinate information that defines a position of a structure in a tomographic plane from a tomographic image including the structure, and that defines a position of an end part of the structure outside the tomographic plane in a direction intersecting the tomographic image.

Abstract: Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.

Abstract: A computer-implemented method for detecting lung abnormalities is provided. The method includes the steps of: acquiring a chest image of a subject; labeling one or more regions of interest (ROIs) in the chest image; segmenting a fine-grained boundary of each of the labeled ROIs; generating a plurality of output matrixes for each of the segmented ROIs; sorting a plurality of prediction scores obtained in the output matrixes, and generating a plurality of recommendations of potential abnormalities for each of the ROIs; and outputting the recommendations, A smart imagery framing and truthing (SIFT) system for implementing the method is also provided.

Abstract: A system and a method for optically detecting a pose of at least one instrument in an operating theater are provided. The method includes determining pose information of the at least one instrument with an optical pose detection device of a surgical microscope or with a microscope-external optical pose detection device, determining whether the pose information is biunique or whether a biunique determination of the pose of the instrument is possible, and evaluating additional information for determining additional pose information, at least for a case where no biunique determination of the pose is possible.

Abstract: A technique of generating surgical information from intra-operatively acquired image data of vertebrae and pre-operatively acquired image data of the vertebrae is presented. A method implementation includes obtaining first image segments each containing a different vertebra, and second image segments each containing a different vertebra. The first image segments have been derived by processing the pre-operatively acquired image data, and the second image segments have been derived by processing the inter-operatively acquired image data. The method includes identifying one of the second image segments and one of the first image segments that contain the same vertebra, and determining a transformation that registers the identified first image segment and the identified second image segment. The method includes generating surgical information based on the transformation and the identified first image segment.

Abstract: A method of treating a bone fracture includes taking a first 2D image of an implant and a reference body with an imaging device; creating a 3D orientation of the reference body and the implant based on the 2D image; determining an actual axis of delivery of a bone screw through an opening extending through the implant; determining an optimal axis of delivery of the bone screw through the opening such that the optimal axis of delivery guides the bone screw toward a medically optimal location; and moving the implant such that the optimal axis of delivery and the actual axis of delivery coincide.

Abstract: An apparatus for the generation of a medical report is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a user query. The memory instructs the processor to receive a user profile comprising a plurality of medical tests. The memory instructs the processor to generate testing data as a function of the plurality of medical tests using an encoder. The memory instructs the processor to generate textual data that is representative of the testing data using a querying transformer model (Q-former). The memory instructs the processor to generate a medical report as a function of the user query and the textual data using a report large language model (LLM).

Abstract: Systems and methods for classification and/or analysis of skin color(s) are provided. The technology integrates artificial intelligence (AI) with dermatology to enhance skin color classification. The unique matching technique aligns skin images with the Fitzpatrick skin type palette through Euclidean distance matching, achieving higher precision in skin color classification compared to conventional methods.

Abstract: A method for selecting training data for training a deep learning model is provided. The method includes steps of: (a) obtaining one or more individual attributes each of which corresponds to each of a plurality of training data included in total training data, and generating a bipartite graph by matching each of the plurality of training data included in the total training data with the individual attributes; and (b) selecting n training data among the total training data, by referring to the bipartite graph, wherein the n is a target number of the training data to be used for training the deep learning model, and wherein the training data selecting device selects the n training data to be used for training the deep learning model such that each cardinal number of each of the individual attributes matched with the n training data is within a predetermined deviation threshold.

Abstract: Disclosed are a diagnostic information processing method and apparatus based on a medical image, and a storage medium, to achieve disease grading based on a medical image. The method includes: acquiring a first lung medical image of a subject; acquiring image parameters of an affected area in the first lung medical image; and determining, according to the image parameters of the affected area, a disease grade of lungs of the subject corresponding to information of the first lung medical image. Using the solution provided by the present invention, the image parameters of the affected area in the first lung medical image can be acquired, and then the disease grade of the lungs of the subject corresponding to the information of the first lung medical image can be determined according to the image parameters of the affected area, so that a disease can be graded based on a medical image.

Abstract: The invention relates to a method for evaluating in post-treatment an ablation of a portion of an anatomy of interest of an individual, the anatomy of interest comprising at least one lesion. The post-treatment evaluation method comprises in particular a step of automatically evaluating a risk of recurrence of the lesion of the anatomy of interest of the individual based on the analysis of a pair of pre-operative and post-operative medical images of the anatomy of interest of the individual by means of automatic learning method of the neural network type, said method being preloaded during a so-called training phase on a database comprising a plurality of pairs of medical images of an anatomy of interest identical to a plurality of individuals, each medical image pair of the database being associated with a recurrence status of a lesion of the anatomy of interest of said patient.

Abstract: A classification method and a classification device for classifying a level of an age-related macular degeneration are provided. The classification method includes the following. An object detection model and a first classification model are pre-stored. A fundus image is obtained. A bounding box is generated in the fundus image according to a macula in the fundus image detected by the object detection model. An intersection over union between a predetermined area and the bounding box in the fundus image is calculated. A classification of the fundus image is generated according to the first classification model in response to the intersection over union being greater than a threshold.

Abstract: An imaging station/booth for automated total body imaging having a small footprint and capable of quickly, efficiently, effectively, and consistently capturing multiple body images of a user or patient over time with minimal assistance from medical staff.

Abstract: Systems and methods relate to processing digital-pathology images. More specifically, aspects of the present disclosure are directed to accessing a whole-slide image depicting a slice of specimen, defining a set of tiles within at least part of the whole-slide image, generating one or more artifact prediction metrics by applying artifact detection to each tile of the set of tiles, wherein each of the one or more artifact prediction metrics corresponds to a predicted level of image quality of part or all of the whole-slide image, generating a quality heat map image corresponding to the whole-slide image, wherein the quality heat map image is based on the one or more artifact prediction metrics, and outputting the quality heat map image.

Abstract: Captured samples of a physical structure or other scene are mapped to a predetermined multi-dimensional coordinate space, and spatially-adjacent samples are organized into array cells representing subspaces thereof. Each cell is classified according to predetermined target-identifying criteria for the samples of the cell. A cluster of spatially-contiguous cells of common classification, peripherally bounded by cells of different classification, is constructed, and a boundary demarcation is defined from the peripheral contour of the cluster. The boundary demarcation is overlaid upon a visual display of the physical scene, thereby visually demarcating the boundaries of a detected target of interest.

Abstract: An image of a sample collection device is captured. The image of the sample collection device is analyzed to determine whether the sample collection device includes a sufficient amount of fluid sample. The image of the sample collection device is validated based on a result of the image analysis.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for efficient processing of visual content using machine learning models. An example method generally includes generating, from an input, an embedding tensor for the input. The embedding tensor for the input is projected into a reduced-dimensional space projection of the embedding tensor based on a projection matrix. An attention value for the input is derived based on the reduced-dimensional space projection of the embedding tensor and a non-linear attention function. A match, in the reduced-dimensional space, is identified between a portion of the input and a corresponding portion of a target against which the input is evaluated based on the attention value for the input. One or more actions are taken based on identifying the match.

Abstract: A method comprises receiving a user input to select a first point and a second point from a medical image, extracting a plurality of blood vessel regions from a plurality of frame images of the medical image, determining a plurality of first regions associated with the first point and a plurality of second regions associated with the second point, determining a first frame image and a second frame image with the contrast agent arriving at the first point and the second point, based on a change in pixel intensity for each of the plurality of first regions and the plurality of second regions, and computing the blood flow velocity from the first point to the second point based on a time interval between the first frame image and the second frame image and on a distance between the first point and the second point.

Abstract: A method of X-ray imaging includes determining energies of photons emitted by an X-ray source and attenuated by an object that are detected by an energy-discriminating radiation detector, generating photon count data by counting a number of detected photons in a plurality of energy bins of the energy-discriminating radiation detector that includes a first energy bin and an adjacent second energy bin, and generating an X-ray image of the object using the photon count data. Detected photons determined to have an energy within a gap region between a maximum energy threshold of the first energy bin and a minimum energy threshold of the second energy bin are not included in the photon count data used to generate the X-ray image of the object.

Abstract: The present subject matter relates to a system (100) and a method (300) for classifying a type of fracture on musculoskeletal X-ray image (101). The system (100) employs a data collection module, a fracture-type classification module including a classification model (207) and a segmentation model (208) based on an artificial intelligence. The data collection module (205) may collect data corresponding to input X-ray images (101), which are then analyzed by a fracture-type classification module (206) using the classification model (207) and the segmentation model (208). Further, the classification model (207) may calculate the classification score and the segmentation module (208) may calculate the segmentation score. The fracture-type classification module may generate the fracture-type score based on the classification score and segmentation score.

Abstract: Methods and systems disclosed herein relate generally to processing images to estimate whether at least part of a tumor is represented in the images. A computer-implemented method includes accessing an image of at least part of a biological structure of a particular subject, processing the image using a segmentation algorithm to extract a plurality of image objects depicted in the image, determining one or more structural characteristics associated with an image object of the plurality of image objects, processing the one or more structural characteristics using a trained machine-learning model to generate estimation data corresponding to an estimation of whether the image object corresponds to a lesion or tumor associated with the biological structure, and outputting the estimation data for the particular subject.

Abstract: A method of training a neural network model includes generating a positive image based on an original image, generating a positive text corresponding to the positive image based on an original text corresponding to the original image, the positive text referring to an object in the positive image, constructing a positive image-text pair for the object based on the positive image and the positive text, constructing a negative image-text pair for the object based on the original image and a negative text, the negative text not referring to the object, training the neural network model based on the positive image-text pair and the negative image-text pair to output features representing an input image-text pair, and identifying the object in the original image based on the features representing the input image-text pair.

Abstract: Provided is a medical image processing device, a processor device, a medical image processing method, and a computer-readable medium capable of obtaining a recognition result related to a region of interest and storing and displaying a medical image in which the recognition result matches an intention of an operator. The medical image processing device includes an image acquisition unit (40) that acquires a medical image (38), an acquired image storage unit (46A) that stores the acquired medical image, a recognition processing unit (42A) that performs recognition processing on the acquired medical image, a user input signal acquisition unit (50) that acquires a user input signal transmitted according to an operation of a user, and a selection unit (48) that selects a medical image from medical images for which a result of the recognition processing related to a region of interest is obtained in a case where the user input signal is acquired.

Abstract: Methods for defining a building internal framework from external imagery are presented, the method including: receiving a number of raw images of a building; processing the number of raw images of the building to establish a number of facades; and determining the building internal framework for each of the number of facades. In some embodiments, the processing the number of raw images includes: for each of the number of façades, selecting a façade; selecting all IR thermography images corresponding with the selected façade; removing distortion from the selected IR thermography images; stitching the selected IR thermography images corresponding with the selected façade together to form a single façade image corresponding with the selected facade; correcting perspective distortion of the single facade image; cropping the single façade image; and receiving dimensions of an actual façade corresponding with the single façade image.

Abstract: Systems and methods for automatically segmenting and detecting a menstrual cycle phase in ultrasound images of anatomical structures that change over a patient menstrual cycle are provided. The method includes acquiring, by an ultrasound probe of an ultrasound system, an ultrasound image of a region of interest having an anatomical structure that changes over a patient menstrual cycle. The method includes automatically segmenting, by at least one processor of the ultrasound system, an anatomical structure depicted in the ultrasound image. The method includes automatically predicting, by the at least one processor, a menstrual cycle phase based on the segmentation of the anatomical structure. The method includes causing, by the at least one processor, a display system to present at least one rendering of the segmented anatomical structure and the predicted menstrual cycle phase.

Abstract: An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (“FFR”) by calculating a ratio of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

Abstract: A computer implemented method is disclosed for identifying a pose of a probe by registering an ultrasound image with volumetric scan data. The volumetric scan data is processed (31) to determine simulated ultrasound images corresponding with a plurality of different poses of the probe. A feature vector is extracted (32) from each of the simulated ultrasound images and from the ultrasound image. The feature vector from the ultrasound image is compared (33) with each feature vector of the simulated ultrasound images to determine a distance or similarity for each simulated ultrasound image. At least one candidate image is selected (34), the at least one candidate image comprising a subset of the simulated ultrasound images that best matches the ultrasound image, based on the distance or similarity. The pose of the probe is identified (35) from the at least one candidate image.

Abstract: The invention relates to a method for determining a model of an extremity, for providing an individually designed orthosis or prosthesis, the method comprising a step of displaying at least one option for data input by a user, in particular in the form of a patient questionnaire. In a further step, the determination of at least one patient-specific input parameter takes place on the basis of the data input. In one step, the creation of at least one raw model takes place using the at least one input parameter. In a further step, the ascertaining of at least one measured parameter of the raw model and, according to one step, the visualizing, in particular displaying, of the raw model and the at least one measured parameter takes place. Furthermore, the invention relates to a computer-readable storage medium and also a system.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. An input medical image in a first modality is received. Features are extracted from the input medical image using a first machine learning based encoding network. A medical imaging analysis task is performed on the input medical image based on the extracted features by, in one embodiment, decoding the extracted features to generate results of the medical imaging analysis task using a machine learning based decoding network. Results of the medical imaging analysis task are output. In one embodiment, the first machine learning based encoding network is jointly trained with a second machine learning based encoding network with an unsupervised loss using unannotated pairs of training images. Each of the unannotated pairs comprise a first training image in the first modality and a second training image in a second modality.

Abstract: An artificial intelligence-based image processing method implemented by a computer device is provided. The method includes: acquiring an image; performing element region detection on the image to determine an element region in the image; detecting a target element region in the image using an artificial intelligence-based technique; generating a target element envelope region by searching an envelope for the detected target element region; and fusing the element region and the target element envelope region to obtain a target element region outline.

Abstract: Example embodiments describe a computer implemented method for obtaining parameterized characteristics of a tissue comprising obtaining at least two weighted MRI volume scans of the tissue, and transforming the two weighted MRI volume scans into parameters of a parameterized voxel-based model of the tissue by combined performing, by a super-resolution imaging technique, constructing a volume comprising voxels of the parameterized voxel-based model, and, by a quantitative MRI modelling technique, constructing the parameters for the respective voxels of the parameterized voxel-based model.

Abstract: Techniques for volumetric assessment of a product based on images captured by a bi-optic scanner are disclosed herein. An example system includes a first imaging assembly, having a field of view of a product scanning region, configured to capture a first image of an item passing through the product scanning region; a second imaging assembly, having an orthogonal field of view, configured to capture a second image of the item passing through the product scanning region at the same time; processors; and a memory storing instructions that, when executed, cause the processors to: determine a first distance of the item from an edge of the first field of view based on the first image; determine a second distance of the item from the a corresponding edge of the second field of view based on the second image; and generate an estimated volume of the item based on the first image, the first distance, the second image, and the second distance.

Abstract: Aspects of the disclosure are directed to the field of artificial intelligence technologies and provides a method and an apparatus for obtaining a feature of duct tissue based on computer vision, an intelligent microscope, a storage medium, and a computer device. The method can include the steps of obtaining an image including duct tissue, determining, in an image region corresponding to the duct tissue in the image, at least two feature obtaining regions adapted to duct morphology of the duct tissue, obtaining cell features of cells of the duct tissue in the feature obtaining regions respectively, and obtaining a feature of the duct tissue based on the cell features of the cells of the duct tissue in the feature obtaining regions respectively.

Abstract: The present disclosure provides methods and systems directed to performing real-time and/or AI-assisted radiology. A method for processing an image of a location of a body of a subject may comprise (a) obtaining the image of the location of a body of the subject; (b) using a trained algorithm to classify the image or a derivative thereof to a category among a plurality of categories, wherein the classifying comprises applying an image processing algorithm; (c) directing the image to a first radiologist for radiological assessment if the image is classified to a first category among the plurality of categories, or (ii) directing the image to a second radiologist for radiological assessment, if the image is classified to a second category among the plurality of categories; and (d) receiving a recommendation from the first or second radiologist to examine the subject based at least in part on the radiological assessment.

Abstract: An image defogging method based on dark channel prior is provided, including: acquiring a dark channel image and a light channel image of a haze weather image according to a haze weather image; selecting pixel values in the light channel image and calculating an atmospheric light value A; obtaining a transmittance image based on the light channel image and the atmospheric light value A; obtaining a restored image of defogging based on the transmittance image and the atmospheric light value A. By using the pixel values of the light channel to distinguish whether pixel points comply with the dark channel prior, calculating the transmittance of image areas that comply with the dark channel prior and those that do not, so as to avoid the situation where the image defogging effect is not good due to inaccurate transmittance estimation, making the defogging of images containing high brightness areas more clear.

Abstract: The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.

Abstract: A computer device obtains a medical image set. The device identifies a difference between the reference medical image and the target medical image to obtain a candidate non-lesion region in the target medical image. The device determines area size information of the candidate non-lesion region as candidate area size information. The device adjusts the candidate non-lesion region according to the annotated area size information when the candidate area size information does not match the annotated area size information, so as to obtain a target non-lesion region in the target medical image.

Abstract: A method for providing a corrected dataset includes receiving a preoperative dataset containing an image and/or a model of an examination region of an examination subject. Length information is received. At least a part of a medical object is arranged intraoperatively in the examination region. The length information includes information relating to a length of the part of the medical object arranged in the examination region. First positioning information relating to a virtual positioning of a predefined section of the part of the medical object arranged in the examination region is determined based on the preoperative dataset and the length information. The method includes receiving and/or determining second positioning information relating to a real positioning of the predefined section. A transformation rule for minimizing a deviation between the first and second positioning information is determined, and the corrected dataset is generated by applying the transformation rule to the preoperative dataset.