Abstract: The disclosure notably relates to a computer-implemented method for machine-learning a classification function configured for detection of mechanical interfaces of a CAD model representing a single mechanical part. The method comprises obtaining a training dataset comprising graphs, each graph representing a CAD model representing a single mechanical part. Each graph comprises one or more nodes representing a canonical surface of the respective CAD model. Each node comprises a label from the list: primary face, secondary face, tertiary face, maintain face, or no interface. Each graph further comprises one or more edges representing a connection between the canonical surfaces. The method further comprises training the classification function based on the dataset to classify, with a label of the label list, each node of a graph representing a CAD model representing a single mechanical part. This provides an improved solution for detecting mechanical interfaces.

Abstract: A computer-implemented method for machine-learning a classification function configured for detection of mechanical interfaces of a CAD model representing a mechanical assembly of parts. The method includes obtaining a training dataset comprising graphs representing a local contact between parts of a CAD model. The graph includes one or more nodes representing canonical surfaces of the CAD model. Each node comprises a label within the label list: primary face, secondary face, tertiary face, maintain face, and no interface. The graph further includes one or more internal edges and one or more external edges. The method further includes training the classification function based on the training dataset to classify, with a label of the label list, each node of a graph representing a local contact between parts of a CAD model representing a mechanical assembly of parts. This provides an improved solution for detecting mechanical interfaces.

Abstract: A computer-implemented method of 3D reconstruction of at least one real object comprising an assembly of parts. The 3D reconstruction method includes obtaining a neural network configured for generating a 3D primitive CAD object based on an input depth image, obtaining a natural image and a depth image representing the real object, segmenting the depth image based at least on the natural image, each segment representing at most a respective part of the assembly, and applying the neural network to each segment.

Abstract: A computer-implemented method for designing a rigid-flex circuit board comprising a set of rigid zones and one or more flexible zones. Each flexible zone connects a respective subset of rigid zones. The rigid-flex circuit board has a folded state and a flattened state. The method includes obtaining a 3D layout of the set of rigid zones, representing the set of rigid zones in the folded state. The method includes defining a respective set of edge segments of the respective subset of rigid zones connected by each flexible zone. The method includes computing a 2D layout of the set of rigid zones, based on the 3D layout and on the respective set of edge segments, representing the set of rigid zones in the flattened state. The method includes defining a respective plane portion representing each flexible zone in the flattened state, based on the 2D layout.

Abstract: A computer-implemented method for measuring an anatomical target from a set of medical images representing the anatomical target. The method includes obtaining at least one medical image of the anatomical target, obtaining an annotation associated with the obtained at least one medical image of the anatomical target, the annotation comprising a measurement of the anatomical target. The method further comprises obtaining a trained model configured for outputting, based on the obtained annotation, a segmentation mask of the anatomical target represented on the obtained at least one medical image. The method includes applying the obtained trained model to the obtained at least one medical image and the obtained annotation. Thereby, the method identifies a segmentation mask of the anatomical target represented on the obtained at least one medical image. The method includes computing a measurement of the anatomical target on the identified segmentation mask.

Abstract: A computer implemented method for improving a similarity search of an industrial component model including obtaining a set of industrial component models, each having associated attributes and a similarity embedding, receiving a similarity request using a given industrial component model as an input, the output of said similarity request being a first subset of industrial component models selected from the set of industrial component models based on the comparison between similarity embeddings and the similarity embedding of the input industrial component model, receiving a second subset of industrial component models from said first subset of industrial component models based on an interchangeability criteria of the input industrial component model with any industrial component model of said second subset of industrial component models, associating a similarity attribute to the input industrial component model, and computing a new set of similarity embeddings.

Abstract: Described is a computer-implemented method for determining a 3D modeled object deformation. The method comprises providing a deformation basis function configured for inferring a deformation basis of an input 3D modeled object. The method further comprises providing a first 3D modeled object. The method further comprises providing a deformation constraint of the first 3D modeled object. The method further comprises determining a second 3D modeled object which respects the deformation constraint. The determining of the second 3D modeled object comprises computing a trajectory of transitional 3D modeled objects between the first 3D modeled object and the second 3D modeled object. The trajectory deforms each transitional 3D modeled object by a linear combination of the result of applying the deformation basis function to the transitional 3D modeled object. The trajectory reduces a loss penalizing an extent of non-respect of the deformation constraint by the deformed transitional 3D modeled object.

Abstract: A method for machine-learning a function configured constraints prediction for a sketch representing at least a portion of a mechanical part. The method includes providing a training dataset of training examples, each including a graph representing a sketch. The graph includes object nodes each representing a geometric object, geometry nodes each representing a geometric property, topological edges each representing a topological link, geometrical edges each connecting a geometry node to one object node, and constraint edges each representing a constraint and labeled with a label of a predefined set of labels each representing a constraint of a predefined constraints set. The method includes training the function to take as input a graph representing a sketch and to output constraints each of a constraint edge of the graph and each belonging to the predefined constraints set.

Abstract: A computer-implemented method for forming a dataset of medical images. The dataset is configured to be used for training a model. The trained model is thereby configured to output a prediction from an input medical image. The dataset-forming method comprises obtaining a first set of medical images. The first set consists of annotated medical images. The first set is unbalanced with respect to a meta-property relative to each medical image. The dataset-forming method further comprises obtaining a second set of medical images. The second set comprises unannotated medical images. The dataset-forming method further comprises outputting a third set of medical images. The third set comprises the first set and unannotated medical images of the second set. The third set is balanced with respect to the meta-property. The dataset-forming method is an improved solution for forming a dataset of medical images.

Abstract: A stamping part design method including obtaining a first B-rep model having a 3D boundary curve formed by boundary-curve edges and obtaining a first and second value representing a g1-continuity and a g2-continuity extrapolation length requirement. The method includes determining a second B-Rep model comprising an extrapolation of the first B-Rep model and presenting at least a g2-continuity over a length of the extrapolation patches equal to the second value and then at least a g1-continuity over a length of the extrapolation patches equal to the first value. The determination of the second B-Rep model includes partitioning the boundary-curve edges into first groups of consecutive first boundary-curve edges, and second groups of consecutive second boundary-curve edges and applying a surface-extension operator to each first group and filling the gaps. This forms an improved solution of stamping part design.

Abstract: A computer-implemented method for generating, from one or more medical images each of an organ of a patient, a medical report compliant with a predefined set of medical report rules. The method includes, automatically, determining positions and diameters of one or more lesions in the one or more medical images. The method further including, automatically, determining, based on determined positions and diameters of the lesions, a list of target lesions in accordance with the predefined set of medical report rules. The method further includes, automatically, generating the medical report based on the determined target lesions and their positions and diameters.

Abstract: A computer-implemented method for processing a medical image of a body of a patient on a horizontal support. The method includes obtaining the medical image. The medical image includes voxels. Each voxel is associated with a value. The value represents tissue density at the position represented by the voxel. The method further includes discarding each voxel of the medical image associated with a value smaller than a predefined threshold. The discarding splits the image into connected components separated by discarded voxels. The method further includes identifying, among the connected components, a connected component having a largest number of voxels. The method further includes computing a curve separating the identified connected component from the remaining part of the medical image. The voxels bounded by the curve correspond to the body of the patient.

Abstract: A computer-implemented method for material extrusion detection in a portion of a mechanical part having a distribution of material. The method includes obtaining a CAD 3D model of the mechanical part including a skin portion representing an outer surface of the portion of the mechanical part and an extrusion axis. The method further includes computing a ratio of an area of an orthogonal projection of the skin portion over an area of the skin portion. The method further includes determining whether or not the distribution of material is arranged as an extrusion based on the ratio and a ratio threshold. The outer surface is determined to be an extrusion surface when the ratio is lower than the ratio threshold. This forms an improved solution for processing a CAD 3D model of a mechanical part.

Abstract: A computer-implemented method for computing a score representing a percentage of occurrence of a clinical event. The method includes obtaining a database including a list of P characteristics within a vector X, associated with the clinical event. The method includes obtaining, for a given patient, the modalities corresponding to a subset of the characteristics in the list. The method includes extracting, from the database, the modality weight associated with each modality obtained for the patient. The method includes computing the score for the given patient by summing the extracted modalities weights, using the formula Scorepatient=?pj?X*?(pj). The method includes computing a dispersion range associated with the computed score. Such a method forms an improved solution for computing a score representing a percentage of occurrence of a clinical event for a patient.

Abstract: A computer-implemented method for machine-learning a neural network having a Spike Neural Network for predicting a time occurrence of a clinical event. The method includes providing a training dataset of training examples. Each training example includes one or more time series of values of respective physiological measures of a patient over a past time period and a corresponding ground truth of a time occurrence of the clinical event. The method further includes training the neural network based on the training dataset. The neural network is trained for taking as input one or more time series of values of the respective physiological measures of a patient over a past time period and for outputting a prediction of probabilities over a time period of the occurrence of the clinical event. The method is an improved solution for predicting a time occurrence of a clinical event.

Abstract: A computer-implemented method for encoding a time series of values of a physiological measure of a patient. The method includes obtaining the time series of values of the physiological measure of the patient. The method further includes, for each time point of the time series of values, obtaining a slope coefficient of the time series of values at the time point. The obtaining a slope coefficient is based on the time point, a predetermined number of previous time points, and a predetermined number of next time points. The method further includes, for each time point of the time series of values, comparing the slope coefficient to a reference slope coefficient. The method is an improved solution for encoding a time series of values of a physiological measure of a patient.

Abstract: A computer implemented method for designing a 3D modeled object representing a manufacturing product. The method includes obtaining a base mesh representing the 3D modeled object, selecting one or more connected edges of the base mesh, subdividing the base mesh based on the selected edges by obtaining a bevel pattern area over the selected path. The method obtains, for at least one of the two endpoints of the path, a transition area by grouping all faces sharing the at least one of the two endpoints of the path, except those of the computed bevel pattern area. The method re-meshes the transition area by obtaining a transition vertex located in the transition area and computing an edge connecting each vertex of the pair of vertices with the obtained transition vertex. The method outputs the subdivided base mesh. This improves the design of a 3D modeled object.

Abstract: A method for generation of a sequence of synthetic ultrasound images of an organ. The method includes providing a video diffusion model trained to take as input a sequence of semantically labelled 2D representations of the organ and a corresponding sequence of noise images, to generate a corresponding denoised sequence of ultrasound images respecting the labels. The method includes providing several consecutive sequences of labelled 2D representations of the organ and corresponding consecutive sequences of noise images, iteratively applying the video diffusion model to each couple formed by one said consecutive sequence and one corresponding noise images sequence, including, at each iteration, using, for each denoising step of the backward process of the video diffusion model, a part of the denoised synthetic ultrasound images generated at the previous iteration for that denoising step in replacement of a part of images of the given couple for that denoising step.

Abstract: A computer-implemented method for predicting new occurrences of an event of a physical system. The method includes providing a first set of past events of the physical system, each past event comprising several attributes, providing a signature for each past event of the first set, providing a new event comprising several attributes, computing a signature of the new event, computing a similarity measure between the signature of the new event and each signature of each past event of the first set, determining the past events closest to the new event according to the similarity measures thereby forming a second set of past events, computing a score of relevance for each attribute of the second set, providing a set of attributes by selecting the attributes having the greater scores of relevance.

Abstract: A method for updating an exploded path within a 3D scene including displaying a 3D scene provided with a three-axis system and including an exploded path extending from a starting point to a finishing point, the exploded path being a connected series of at least three line segments each aligned with one of the axes (X, Y, Z) of the three-axis system according to a periodic direction pattern; and, upon instruction to apply a translation to a line segment: applying the translation to this line segment and to the following or preceding line segment to preserve the periodic direction pattern, and, if necessary, adding a linking line segment to relink the exploded path to the starting or finishing point.