Abstract: The present application provides an image reconstruction method, a device, equipment, a system, and a computer-readable storage medium. Said method comprises: obtaining a target reconstruction model (S1); invoking a first convolutional layer in the obtained target reconstruction model to extract shallow layer features from the obtained image to be reconstructed (S2); invoking a residual network module in the target reconstruction model to obtain middle layer features from the shallow layer features (S3); invoking a densely connected network module in the target reconstruction model to obtain deep layer features from the middle layer features (S4); and invoking a second convolutional layer in the target reconstruction model to perform image reconstruction on the deep layer features so as to obtain a reconstructed image of the image to be reconstructed (S5). Said method improves the quality and resolution of a reconstructed image.

Abstract: This invention provides a histologic system and method for rapidly and accurately assessing tumor margins for the presence or absence of tumor using machine learning algorithms. This affords a rapid and accurate histologic tumor readout and increase process efficiency and decreases the chance for human error. Advantageously and uniquely, the system and method allows for analyzing the tissue section as complete or incomplete as the first criteria to determine whether a tissue section is clear of tumor. A machine learning process receives whole slide images (WSI) of tissue and determines (a) if each image of the WSI contains complete/incomplete tissue samples and (b) if each image of the WSI contains tumorous tissue or an absence thereof. A reconstruction process generates a model of the tissue that maps types of tissue therein, and a display process provides results of the model or report for use and manipulation by a user.

Abstract: The present disclosure relates to: a method for evaluating a state of a motor function of a patient who has or is suspected of having brain damage by using parameters obtained from a diffusion weighted image of the patient's brain; a program for executing the method in a computer; and an image processing device and an MRI device which can be used in practicing the method. According to the present disclosure, the motor function state of a patient who has or is suspected of having brain damage can be evaluated, and it is also possible to predict the recovery of the motor function after regeneration treatment. Accordingly, whether the patient is suitable for the regeneration treatment can be determined before the start of the treatment.

Abstract: Techniques for representing a scene or map based on statistical data of captured environmental data are discussed herein. In some cases, the data (such as covariance data, mean data, or the like) may be stored as a multi-resolution voxel space that includes a plurality of semantic layers. In some instances, individual semantic layers may include multiple voxel grids having differing resolutions. Multiple multi-resolution voxel spaces may be merged to generate combined scenes based on detected voxel covariances at one or more resolutions.

Abstract: A cabinet X-ray image system for obtaining colorized or greyscale density X-ray images of a specimen has a cabinet defining an interior chamber. The cabinet includes a walled enclosure surrounding the interior chamber, a door configured to cover the interior chamber and a sampling chamber for containing the specimen. The system further includes a display and an X-ray unit. The X-ray unit includes an X-ray source, an X-ray detector and a specimen platform configured to receive the specimen. Moreover, the system includes a controller configured to selectively energize the X-ray source, control the X-ray detector to collect the X-rays, determine the density of different areas of the specimen and create a density X-ray image of the specimen. The density X-ray image is colorized or greyscale. The controller is also configured to selectively display the density X-ray image of the specimen on the display.

Abstract: The present disclosure relates to a method for modeling a personalized breast implant including the operations of: obtaining medical image data and body scan data; obtaining first image data by 3D modeling the medical image data; obtaining second image data by 3D modeling the body scan data; creating third image data by merging the first image data and the second image data; and creating breast implant appearance information on the basis of the third image data.

Abstract: Embodiments described herein are generally related to a method and a system for constructing and delivering a pulse to perform an entangling gate operation between two trapped ions during a quantum computation, and more specifically, to a method of demodulating and spline interpolating a pulse that can be practically implemented in the system while increasing the fidelity of the entangling gate operation, or the probability that at least two ions are in the intended qubit state(s) after performing the entangling gate operation between the two ions.

Abstract: Provided are a computer program, a method, and a server in which a possibility that a load on display control increases is reduced, compared to the related art. According to an exemplary embodiment of such a configuration, first data relevant to a first position in a virtual space in which a first avatar manipulated by using a first terminal of a first user exists is acquired, whether or not a first condition is satisfied is determined, second data is received in a case where it is determined that the first condition is satisfied, control data for controlling a display unit of the first terminal such that at least the virtual object is displayed on the display unit of the first terminal is decided on the basis of the second data, and the display unit is controlled on the basis of the control data.

Abstract: In some embodiments, the systems and methods of the disclosure can efficiently and accurately classify neurodegenerative disorder(s) and/or movement disorder(s) of a subject (e.g., a patient) using at least quantitative features associated with one or more regions of interest determined from one or more sets of image data of the subject's brain. The method may include processing one or more sets of MRI image data of the subject's brain to extract one or more quantitative features for one or more regions. The one or more quantitative features may include a first quantitative and a second quantitative feature. The method may further include classifying at least the one or more quantitative features into one or more classes associated with neurodegenerative dementia disorder, neurodegenerative movement disorder, non-neurodegenerative movement disorder and/or heathy control. The method may include generating a report including a classification of at least the one or more quantitative features.

Abstract: An ultrasound device (10) includes a probe (12) including a tube (14) sized for insertion into a patient and an ultrasound transducer (18) disposed at a distal end (16) of the tube. A camera (20) is mounted at the distal end of the tube in a fixed spatial relationship to the ultrasound transducer. At least one electronic processor (28) is programmed to: control the ultrasound transducer and the camera to acquire ultrasound images (19) and camera images (21) respectively while the ultrasound transducer is disposed in vivo inside the patient; and construct a keyframe (36) representative of an in vivo position of the ultrasound transducer including at least ultrasound image features (38) extracted from at least one of the ultrasound images acquired at the in vivo position of the ultrasound transducer and camera image features (40) extracted from one of the camera images acquired at the in vivo position of the ultrasound transducer.

Abstract: A method for detecting the presence of an atheroma in an artery of interest using an ultrasound apparatus can include performing a first procedure on a first day. The first procedure can include detecting one or more blood vessels in a target region of a patient's body. The procedure can include identifying, from the one or more blood vessels, an artery of interest; automatically detecting spatial boundaries of constituent layers of an arterial wall of the artery of interest and calculating, via a processor of the ultrasound apparatus, a cross-sectional intima-media area (IMA) of the artery of interest at a first location along a length of the artery of interest. The procedure can include automatically calculating, via the processor of the ultrasound apparatus, IMA of the artery of interest at a second or more locations along the length of the artery of interest.

Abstract: The disclosed method includes acquiring sample models and marking sample feature points; selecting target feature points and regional points on bones; transforming the target feature points and the regional points, and registering the target feature points with the sample feature points; formulating a strategy for assigning impact factors to the sample models, linearly combining all sample models to construct an initial model, and determining initial feature points corresponding to the target feature points in the initial model; adjusting the strategy according to the distance between initial feature points and the target feature points, selecting the strategy corresponding to the minimum distance as an optimal strategy, and determining an optimal initial model; determining a matching point corresponding to each regional point in the optimal initial model, and calculating a transformation relationship; and transmitting all points of the optimal initial model according to the transformation relationship to obt

Abstract: A processor detects a structure of interest from a plurality of tomographic images indicating a plurality of tomographic planes of an object. The processor selects a tomographic image from the plurality of tomographic images according to a type of the structure of interest in a region in which the structure of interest has been detected and generates a composite two-dimensional image using the selected tomographic image in the region in which the structure of interest has been detected and using a predetermined tomographic image in a region in which the structure of interest has not been detected.

Abstract: A system and method for creating multi-phase kidney computed tomography (CT) images of a patient are provided. The method includes acquiring or accessing CT data that includes a first dataset acquired from the patient during a first phase when a dose of a contrast agent is being excreted from a kidney of the patient and does not include a second dataset acquired from the patient during a second phase after the dose of the contrast agent is delivered but before the dose of a contrast agent is being excreted from a kidney.

Abstract: A medical image data creation apparatus for training acquires a first medical image, acquires a second medical image that is different from the first medical image and is obtained by photographing a substantially same spot as in the first medical image, creates lesion region information concerning a lesion part in the second medical image, and creates medical image data for training in which the first medical image and the lesion region information are associated with each other.

Abstract: Systems and methods for image processing are provided in the present disclosure. The systems may generate a preliminary image by filtering image data generated by an image acquisition device. The system may generate an intermediate image by performing, based on a first objective function, a first iterative operation on the preliminary image. The first objective function may include a first term associated with a first difference between the intermediate image and the preliminary image, a second term associated with continuity of the intermediate image and a third term associated with sparsity of the intermediate image. The systems may also generate a target image by performing, based on a second objective function, a second iterative operation on the intermediate image. The second objective function may be associated with a system matrix of the image acquisition device and a second difference between the intermediate image and the target image.

Abstract: Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. It incorporates a novel correlation network composed of convolutional layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The system includes an angle optimizer to further enhance the compressibility of an image and an error resilience subsystem to improve robustness against transmission errors and data loss. The convolutional layers extract multi-dimensional features from the image, while the channel-wise transformer learns global inter-channel relationships. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission.

Abstract: A method and system for identifying an implant in an x-ray image of an anatomy shown on a display screen. The method includes: displaying the image including an artifact of the implant; prompting a user to enter parameters of the implant; displaying a rendering of the implant over the image; prompting the user to position the rendering on the artifact at an accurate position corresponding to an actual position of the implant; alerting the user when the rendering is at an inaccurate position; prompting the user to reposition the rendering from the inaccurate position to the accurate position; alerting the user when the rendering is maneuvered by the user to the accurate position; and after the rendering has been maneuvered to the accurate position by the user, reconstructing the x-ray image to replace the artifact with the rendering affixed in the accurate position.

Abstract: An ultrasound image acquisition method, comprising: according to a first collection instruction, determining a working mode of an ultrasound image acquisition system to be a first mode; in the first mode, transmitting a first ultrasound wave to interior of a to-be-detected object; obtaining a first ultrasound echo signal based on the first ultrasound wave that is returned from the interior of the to-be-detected object, and combining beams thereof to obtain first basic ultrasound wave images; according to the first basic ultrasound images, acquiring a first reference direction and the contour of a reference to-be-detected sub-object; according to the first reference direction and the contour of the reference to-be-detected sub-object, acquiring first spatial position information of the interior of the to-be-detected object; according to the first spatial position information, generating and displaying a first stereoscopic ultrasound image and/or a first planar ultrasound image based on the interior of the to-b

Abstract: The generation device 100 according to the present application includes an acquisition unit 131 and a generation unit 134. The acquisition unit 131 acquires a first pathological image captured and an annotation that is information added to the first pathological image and is meta information related to the first pathological image. The generation unit 134 generates learning data for evaluating pathological-related information based on a second pathological image according to the second pathological image different from the first pathological image, the learning data being learning data obtained by transcribing the annotation in a manner corresponding to the second pathological image.

Abstract: Herein disclosed are robotic dentistry methods comprising tooth extraction and crown preparation. Robotic tooth extraction may comprise determining if the correct tooth is targeted, based on a scanned image; determining a decay state of the tooth based on the scanned image; selecting an extraction technique determined as a function of the decay state; and extracting the tooth using the selected extraction technique. Robotic crown preparation may comprise receiving an initial reduction plan to reduce a tooth; presenting, to a dentist for approval, the initial reduction plan for approval; upon approval, reducing the tooth according to the approved initial reduction plan; upon rejection, modifying the initial reduction plan based on dentist input; receiving the modified reduction plan to reduce the tooth; presenting, to a dentist for approval, the modified reduction plan for approval; and upon approval of the modified reduction plan, reducing the tooth according to the approved modified reduction plan.

Abstract: A medical image data processing apparatus comprising processing circuitry configured to: receive medical image data; segment a body part included in the medical image data into multiple regions; refine or constrain the segmentation based on at least one plane to obtain a segmentation that includes at least one boundary or other feature having a desired property.

Abstract: A training data modification system (TDM) for machine learning and related methods. The system comprises a data modifier (DM) configured to perform a modification operation to modify medical training X-ray imagery of a patient. The modification operation causes image structures in the modified medical training imagery. The image structure is representative of a property of i) a medical procedure, ii) an image acquisition operation by an X-ray-based medical imaging apparatus (IA), iii) an anatomy of the patient.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: A method for diagnostic imaging reconstruction uses a prior image xpr from a scan of a subject to initialize parameters of a neural network which maps coordinates in image space to corresponding intensity values in the prior image. The parameters are initialized by minimizing an objective function representing a difference between intensity values of the prior image and predicted intensity values output from the neural network. The neural network is then trained using subsampled (sparse) measurements of the subject to learn a neural representation of a reconstructed image. The training includes minimizing an objective function representing a difference between the subsampled measurements and a forward model applied to predicted image intensity values output from the neural network. Image intensity values output from the trained neural network from coordinates in image space input to the trained neural network are computed to produce predicted image intensity values.

Abstract: Provided is a medical image analysis method including acquiring a target medical image, detecting a target joint spacing region from the target medical image, acquiring a first value related to a width of a joint part from the target medical image, acquiring a second value related to joint spacing from the target joint spacing region, and calculating a target joint condition indicator indicating a joint condition based on the first value and the second value.

Abstract: A computer-implemented method for facilitating opportunistic screening for cardiomegaly includes obtaining a set of computed tomography (CT) images. The set of CT images captures at least a portion of a heart of a patient, and the set of CT images is captured for a purpose independent of assessing cardiomegaly. The method further includes using the set of CT images as an input to an artificial intelligence (AI) module configured to determine a heart measurement based on CT image set input. The method also includes obtaining heart measurement output generated by the AI module and, based on the heart measurement output, classifying the patient into one of a plurality of risk levels for cardiomegaly. The classification is operable to trigger additional action based on the corresponding risk level for the patient.

Abstract: An exemplary image registration system identifies a subsurface structure at a surgical site based on subsurface imaging data from a subsurface image scan at the surgical site. The image registration system uses the identified subsurface structure at the surgical site for a registration of endoscopic imaging data from an endoscopic imaging modality with additional imaging data from an additional imaging modality. Corresponding systems and methods are also disclosed.

Abstract: In part, the disclosure relates to computer-based methods, devices, and systems suitable for detecting a delivery catheter using intravascular data. In one embodiment, the delivery catheter is used to position the intravascular data collection probe. The probe can collect data suitable for generating one or more representations of a blood vessel with respect to which the delivery catheter can be detected.

Abstract: Various computational methods and techniques are presented to increase the lateral and axial resolution of an ultrasound imager in order to allow a medical practitioner to use an ultrasound imager in real time to obtain a 3D map of a portion of a body with a resolution that is comparable to an MRI machine.

Abstract: A computer-implemented method of an embodiment is for automatically estimating and/or correcting an error due to artifacts in a multi-energy computed tomography result dataset relating to at least one target material. In an embodiment, the method includes determining at least one first subregion of the imaging region, which is free from the target material and contains at least one, in particular exactly one, second material with known material-specific energy dependence of x-ray attenuation; for each first subregion, comparing the image values of the energy dataset for each voxel, taking into account the known energy dependence, to determine deviation values indicative of artifacts; and for at least a part of the at least one remaining second subregion of the imaging region, calculating estimated deviation values by interpolating and/or extrapolating from the determined deviation values in the first subregion, the estimated deviation values being used as estimated error due to artifacts.

Abstract: A medical image processing method and processing apparatus, and a computer readable storage medium. The method includes: obtaining a to-be-processed image; performing a feature extraction on the to-be-processed image to obtain a corresponding feature image; and re-determining a pixel value of each pixel in the to-be-processed image based on first information and second information of a corresponding pixel in the feature image, and processing the to-be-processed image; wherein the first information is information of a pixel adjacent to the corresponding pixel in the features image, and the second information is information of a pixel that is not adjacent to and is similar to the corresponding pixel in the features image.

Abstract: A flexible coil includes a flexible substrate with a plurality of holes extending through the substrate. Each hole is configured to receive at least a portion of a fastener extending through the hole. Each fastener engages the flexible substrate and an antenna loop to positively retain the antenna loop to the flexible substrate. The holes are arranged in the flexible substrate to align each antenna loop with respect to the other antenna loops mounted to the flexible substrate. The fasteners are removably mounted to the flexible substrate such that the fastener positively retains the antenna loop to the flexible substrate when mounted to the flexible substrate but allows individual antenna loops to be removed from the flexible substrate when the fastener is removed from the flexible substrate. Multiple fasteners are provided for each antenna loop and are spaced apart from each other and positioned along the length of the loop.

Abstract: A method for denoising an image, an apparatus, and a computer-readable storage medium. The method includes: dividing a to-be-processed image into a plurality of image areas; for each image area, expanding an edge of the image area to obtain an expanded area; wherein the expanded area comprises the image area and an edge area, and the edge area comprises pixels in another image area adjacent to the image area; for each expanded area, performing denoising processing on the expanded area through a preset algorithm to obtain a denoised expanded area; wherein the denoised expanded area comprises a denoised image area and a denoised edge area; and abandoning the denoised edge area in each denoised expanded area, and retaining and combining all the denoised image areas to form a denoised image.

Abstract: A medical image processing apparatus and a medical image processing method are provided which are capable of clearly presenting a distal end of a medical device in a tomosynthesis image of an object under examination into which the medical device is inserted. The medical image processing apparatus handles a tomosynthesis image generated using a plurality of projection images obtained by imaging an object under examination in an angle range of less than 180 degrees, and includes: a storage section for pre-storing blur data at individual imaging space coordinates; and a correction section for correcting the tomosynthesis image using the blur data.

Abstract: A learning image generation device including: a supervised data acquisition unit that acquires supervised data including a learning image and a correct learning image in which a correct region is defined in the learning image as a pair; and a variation learning image generation unit that generates a variation learning image in which a pixel value of a pixel belonging to the correct region is varied within a limitation range of an allowable pixel value of the pixel belonging to the correct region in the learning image.

Abstract: An apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy including an overlay. The apparatus includes at least a processor configured receive a set of images of a cardiac anatomy pertaining to a subject, generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of shape parameters using the shape identification model, generate a 3D model of the cardiac anatomy based on the set of shape parameters, generate a map by determine a level of uncertainty at each location of a plurality of locations on the generated 3D model, and overlay the map onto the 3D model.

Abstract: At least one example embodiment relates to a computer-implemented method for providing stroke information, the method comprising receiving computed tomography imaging data of an examination area of a patient, the examination area of the patient comprising a plurality of brain regions, at least one brain region of the plurality of brain regions being affected by a stroke, receiving brain atlas data, generating registered imaging data based on the computed tomography imaging data and the brain atlas data, the registered imaging data being registered to the brain atlas data, generating the stroke information regarding the stroke based on a set of algorithms and the registered imaging data, and providing the stroke information.

Abstract: The present invention relates to the identification of one or more candidate signs indicative of an NTRK oncogenic fusion within patient data. Subject matter of the present invention are a computer-implemented method, a system, and a non-transitory computer-readable storage medium for determining a probability value from patient data associated with a subject patient, the probability value indicating the probability of the subject patient suffering from cancer caused by a mutation of a neurotrophic receptor tyrosine kinase (NTRK) gene.

Abstract: A system and a method for measuring a maturation stage of a biological organ are based on quantitative MR maps for the organ. The method includes acquiring with a first interface and for a subject, a quantitative MR map for the organ. The quantitative MR map includes voxels each characterized by a quantitative value. The quantitative value of each voxel represents a measurement of a physical or physiological property of a tissue of the biological organ for the voxel. The method also includes applying to the quantitative map a trained function to estimate the subject organ maturation stage, and the trained function outputting an age. The method provides with a second interface the maturation stage of the organ of the subject as being the output age.

Abstract: A method for processing breast tissue image data includes processing image data of a patient's breast tissue to generate a high-dimensional grid depicting one or more high-dimensional objects in the patient's breast tissue; determining a probability or confidence of each of the one or more high-dimensional objects depicted in the high-dimensional grid; and modifying one or more aspects of at least one of the one or more high-dimensional objects based at least in part on its respective determined probability or confidence to thereby generate a lower-dimensional format version of the one or more high-dimensional objects. The method may further include displaying the lower-dimensional format version of the one or more high-dimensional objects in a synthesized image of the patient's breast tissue.

Abstract: A computer-implemented method and system for predicting orthodontic results based on landmark detection includes: acquiring a crown point cloud, a set of tooth point clouds, and tooth labels; extracting global dentition features and local tooth features, performing feature fusion on the global dentition features, tooth labels of individual teeth, and the local tooth features of the individual teeth to obtain fused features of the individual teeth, and extracting landmarks of the individual teeth and offset vectors from points in the tooth point clouds to the landmarks; fusing the landmarks of the individual teeth and the tooth point clouds, extracting tooth attention features, acquiring dentition attention features, and fusing the dentition attention features, the global dentition features, and the local tooth features to obtain a point cloud with fused landmarks; and acquiring pre- and post-treatment rigid transformation parameters, and obtaining a post-treatment crown model prediction result.

Abstract: Systems and methods of guiding a clinician in a medical procedure for a nodule involve receiving three-dimensional (3D) image data, generating a volumetric vector map based on the 3D image data, and displaying the volumetric vector map in a way, e.g., via a heat map, that assists a clinician in performing a medical procedure. The systems and methods involve identifying volumetric parameters of the nodule in the 3D image data, generating a voxel map based on the volumetric parameters, identifying a maximum attenuation value in the 3D space of the voxel map, applying a differential equation, e.g., a gradient, to the 3D space of the voxel map from a voxel with the maximum attenuation value to other voxels within the voxel map, and generating a volumetric vector map based on the result of applying the differential equation.

Abstract: The following relates generally to motion prediction in magnetic resonance (MR) imaging. In some embodiments, a “modular” approach is taken to motion correction. That is, individual motion sources (e.g., a patient's breathing, heartbeat, stomach contractions, peristalsis, and so forth) are accounted for individually in the motion correction. In some embodiments, to correct for a particular motion source, a reference state is created from a volume of interest (VOI), and other states are created and deformably aligned to the reference state.

Abstract: A system and method for reviewing a tomosynthesis image data set comprising volumetric image data of a breast, the method comprising, in one embodiment, causing an image or a series of images from the data set to be displayed on a display monitor and selecting or indicating through a user interface an object or region of interest in a presently displayed image of the data set, thereby causing an image from the data set having a best focus measure of the user selected or indicated object or region of interest to be automatically displayed on the display monitor.

Abstract: Systems and methods for performing an assessment of a lesion are provided. A plurality of input medical images of a lesion is received. The plurality of input medical images comprises an initial input medical image and one or more additional input medical images. The initial input medical image comprises a region of interest around the lesion. A mask of the lesion is curated for the initial input medical image based on the region of interest and a set of candidate masks. The region of interest in the initial input medical image is propagated to the one or more additional input medical images based on prior registration transformations. A mask of the lesion is curated for each of the one or more additional input medical images based on the propagated regions of interest and the set of candidate masks. One or more assessments of the lesion are performed based on the mask for the initial input medical image, the masks for the one or more additional input medical images, and prior assessments of lesions.

Abstract: Devices, systems, and methods used to register medical image data with anatomical positions are disclosed. The image data is captured by an imaging system over a period of time. The positions are determined by one or more modalities over the period of time. The positions are collected into position data, and determinations are made as to which position data is best for registering the image data with patient anatomy and for reconstructing 3D images of the patient anatomy.

Abstract: A system for facilitating lesion analysis accesses a first data structure comprising a plurality of entries including anatomic location information and annotation information associated with a plurality of lesions represented in a first set of cross-sectional images. The system displays a respective representation of each of the plurality of entries and presents a second set of cross-sectional images. The system receives user input triggering selection of a particular entry of the plurality of entries of the first data structure.

Abstract: A system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.

Abstract: A personalized naturally designed mitral valve prosthesis and a method for manufacturing the such to precisely fit a specific patient for which the valve prosthesis is made for, is provided. The method includes measuring size and shape of a mitral valve of the specific patient by using imaging means, building a 3D model of the personalized mitral valve prosthesis, optimizing the 3D model using FEM method and fabricating the personalized mitral valve prosthesis by cutting and connecting the annular ring, leaflets and chords to form a personalized prosthesis mitral valve.