Patent Description:
An X-ray system, e.g. a radiography system or a fluoroscopy system, comprises an X-ray source and an X-ray detector. The examination object, in particular a patient, is arranged between the X-ray source and the X-ray detector so that an X-ray image of an examination region can be acquired.

A mammography system includes an X-ray source and an X-ray detector, with a breast arranged between the X-ray source and the X-ray detector. The examination area comprises the breast, especially the entire breast. In general, the breast is compressed or fixed by means of a compression unit. For this purpose, in particular, the surface of the X-ray detector or a housing of the X-ray detector and an essentially parallel arranged compression plate can be used. Furthermore, other forms of the compression unit are known. An X-ray data set can be generated. The X-ray data set may include a digital full-field mammography image and / or a tomosynthesis image.

Modern mammography systems offer a tomosynthesis function to generate a series of projections that allow a three-dimensional representation of the breast using reconstructed layers. Here, the X-ray emitter or the X-ray source moves in an angular range of <NUM> to <NUM> degrees over the compressed breast. The digital full-field mammography image and the tomosynthesis image can also be acquired together within a single compression (per breast).

One of the major problems in radiological (x-ray) examinations is the high rate of rejected images due to poor image quality. According to one study by <NPL>), <NUM> % of images are rejected due to positioning errors. A rejected image does not contribute to diagnostics, and therefore is a useless image which causes additional exposure of patients to obtain a correctly positioned image. For this reason, it is important to have a technique to improve acquisition accuracy for x-ray exams through quality analysis and feedback about position accuracy.

Among radiographic images, knee images have the highest relative rate of being rejected/deleted (e.g. <NUM>,<NUM>% according to Hofmann) with the most prevalent reason being 'positioning of patient or system'. Many images are rejected despite being adequate for medical decisions.

There are several solutions and techniques to address positioning problems for x-ray images. In general, most of the current solutions can be categorized in one of the two following approaches:
The first approach relies on segmentation of different organs and landmarks in the x-ray image and by having those landmarks, one can define some general rules based on measurements to evaluate different positioning criteria. Such solutions are heavily dependent on the accuracy of landmark detection and segmentation models, and only a few millimeters error can lead to wrong positioning evaluation. Within a knee segmentation a small error of fibular head segmentation can lead to two different positioning evaluations.

The second approach does not require segmentation masks and can evaluate different positioning criteria by only having the input image. In this approach, different models for different criteria have been trained to categorize input image, let us consider the previous example for fibular head overlap in knee images. A model should be trained in a way to evaluate rotation of knee based on this criterion by observing huge dataset. Still, the problem remains that the user can not follow and/or influence the determination of the positioning analysis.

Current AI based solutions use black-box models and provide results that are difficult to interpret or explain, leading to concerns regarding their trustworthiness and reliability. This hampers the widespread acceptance of AI based solutions in applications such as image positioning. There have been efforts to interpret or explain the AI model decision-making process using feature/saliency maps to indicate the importance of various image regions toward the model output. These saliency maps typically back-propagate gradients and project them onto the image plane, allowing the user to visualize an approximation of the underlying process followed by the AI model. Saliency maps attempt to improve AI interpreta-bility but do not address AI transparency and explainability in a comprehensive manner. Such AI-based approaches are, for instance, known from <CIT>.

It is an object of the invention to provide a computer-implemented method for providing a positioning score regarding a positioning of an examining region in an X-ray image, a scoring system, a computer program product, a computer-readable medium, and an X-ray system, which allows the user to understand and follow the steps towards the determination of the positioning score.

The object of the invention is solved by a computer-implemented method for providing a positioning score regarding a positioning of an examining region in an X-ray image according to claim <NUM>, a scoring system according to claim <NUM>, a computer program product according to claim <NUM>, a computer-readable medium according to claim <NUM>, and an X-ray system according to claim <NUM>.

In the following the solution according to the invention is described with respect to the claimed scoring systems as well as with respect to the claimed methods. Features, advantages or alternative embodiments herein can be assigned to the other claimed objects and vice versa. In other words, claims for the scoring systems can be improved with features described or claimed in the context of the methods. In this case, the functional features of the method are embodied by objective units of the scoring system.

Furthermore, in the following the solution according to the invention is described with respect to methods and systems for providing a positioning score regarding a positioning of an examining region in an X-ray image as well as with respect to methods and systems for the training of the trained functions. Features, advantages or alternative embodiments herein can be assigned to the other claimed objects and vice versa. In other words, claims for methods and systems for training of the trained function can be improved with features described or claimed in context of the methods and systems for providing a positioning score regarding a positioning of an examining region in an X-ray image, and vice versa.

In particular, the trained function of the methods and systems for providing complete set of second key elements in an X-ray image can be adapted by the methods and systems for training of the trained function. Furthermore, the input data can comprise advantageous features and embodiments of the training input data, and vice versa. Furthermore, the output data can comprise advantageous features and embodiments of the output training data, and vice versa.

The inventors found that especially for knee examinations, the positioning is crucial. As a reason, this invention would focus more on knee x-ray images as a major application for proposed solution. It is noteworthy to mention that the proposed solution can be extended for use in all x-ray examinations.

The invention relates to a computer-implemented method for providing a positioning score regarding a positioning of an examining region in an X-ray image, comprising:.

According to an embodiment of the invention, the X-ray image is a two-dimensional X-ray image.

According to an embodiment of the invention, the first trained function is based on an object detection network, preferably the RetinaNet.

According to an embodiment of the invention, the second trained function and/or the third trained function is based on a classifier, preferably the Densenet, or a regression model.

According to an embodiment of the invention, the initial convolution layer is modified to focus the network attention based on the heatmap and/or the score-weighted heatmap.

According to an embodiment of the invention, the heatmap and/or the at least one region of interest is displayed.

According to an embodiment of the invention, the user can adjust the heatmap and/or the at least one region of interest.

According to an embodiment of the invention, the position scored describes a rotation of the examining region.

According to an embodiment of the invention, the examining region comprises a knee, the thorax, or a breast.

The invention further relates to a scoring system, comprising:.

The invention further relates to a computer program product comprising instructions which, when the program is executed by a scoring system, cause the scoring system to carry out the method according to the invention.

The invention further relates to a computer-readable medium comprising instructions which, when executed by a scoring system, cause the scoring system to carry out the method according to the invention.

The invention further relates to an X-ray system comprising the scoring system according to the invention.

According to an embodiment of the invention, the X-ray system is a radiography system or a mammography system.

Disclosed herein is further a computer-implemented method for providing a third trained function, comprising:.

Disclosed herein is furhter a training system, comprising.

In general, parameters of a trained function can be adapted by means of training. In particular, supervised training, semi-supervised training, unsupervised training, reinforcement learning and/or active learning can be used. Furthermore, representation learning (an alternative term is "feature learning") can be used. In particular, the parameters of the trained functions can be adapted iteratively by several steps of training.

In particular, a trained function can comprise a neural network, a support vector machine, a decision tree and/or a Bayesian network, and/or the trained function can be based on k-means clustering, Q-learning, genetic algorithms and/or association rules. In particular, a neural network can be a deep neural network, a convolutional neural network or a convolutional deep neural network. Furthermore, a neural network can be an adversarial network, a deep adversarial network and/or a generative adversarial network.

The idea behind this invention is to build a transparent, in-terpretable and explainable AI model for positioning check in radiological images or X-ray images in general. In the current manual workflow, an expert user performs the following steps:.

This invention formulates an analogous AI-based workflow with explicit sub-tasks corresponding to each of the steps in the manual workflow.

A modular pipeline for AI-based positioning check is proposed. A radiographic image or an X-ray image is used as input. The expected output is the overall score indicating the quality of positioning for the given image. The AI-based sub-tasks are encapsulated by three modules:.

The first trained function which can be called the ROI Detector uses radiographic image or an X-ray image as input, and provides relevant ROIs in the image as output. The first trained function or the ROI detector detects and identifies all relevant ROIs present in the image. This module can be realized using an available object detection network such as the RetinaNet.

The bank of second trained functions which can be called a bank of ROI scoring modules uses a radiographic image or an X-ray image as well as a ROI heatmap as input and provides individual ROI scores as output.

The second trained functions or the ROI scoring modules score each available ROI by focusing attention on the corresponding image region underlying the ROI. This is achieved by using an additional input based on a heatmap representation of the ROI. An ROI scoring module can be realized using an available classifier architecture such as the Densenet and modifying the initial convolution layer to focus the network attention based on the ROI heatmap. The classifier architecture can be replaced with a regression model to increase the scoring sensitivity from a categorical to a continuous range.

The third trained function can be called the overall scoring module. The third trained function uses a radiographic image or an X-ray image as well as a score-weighted ROI heatmap as input and provides the final positioning score as output.

The overall scoring module computes the final positioning score for the input radiographic image by focusing attention on the image region underlying all available ROIs and their corresponding scores. This is achieved by using an additional input based on an aggregated score-weighted heatmap representation of all available ROIs. An overall scoring module can be realized using an available classifier architecture such as the Densenet and modifying the initial convolution layer to focus the network attention based on the aggregated, score-weighted heatmap. The classifier architecture can be replaced with a regression model to increase the scoring sensitivity from a categorical to a continuous range.

To explain the idea behind the invention, three applications have been chosen, to show how proposed method can be used to assess positioning criteria.

As it was mentioned before, knee positioning check is the main application for proposed method. Two of the most important criteria in knee x-ray images is the position of Fibular head and the location of Patellar center. Both landmarks are used to evaluate rotation in knee images. For first criteria, the amount of overlap between Fibular head and Tibia could indicate rotation. In this case, when the amount of overlap is less than or more than a certain range (e.g. <NUM>% of Fibular width) this could indicate internal or external rotation respectively. In second criteria, location of Patellar is compared to center of Femur. In case of not being exactly in the center, one can assume that there is a possibility of rotation. Our proposed method can be used in this application by first detecting the region of interests by the ROI detector and then categorizing those ROIs (ROI scoring) and finally judge the overall rotation of knee by having all previous results (Overall scoring).

Another application could be evaluating the rotation of chest x-ray images. Here, position of vertebra column and Clavicles are compared to each other to estimate rotation. By having the ROI of this specific region and categorizing it, one can define rotation of whole image.

Another application is related to positioning check of mammography images. In this case, location of nipple and the in-framammary fold (IMF) shape is important to evaluate the position of breast. Again, one can evaluate these positioning criteria by defining ROIs and respective scores to them.

The proposed method provides insights into the decision-making process by deconstructing the task into its component steps and providing corresponding intermediate outputs as explanations. The component steps are designed based on the workflow of an expert user and provide transparency, inter-pretability and explicit explainability to our AI model's decision-making process.

The proposed method additionally allows for expert intervention or feedback at any step of the AI model's decision-making process by allowing the expert user to change the ROI (region of interest) definition at any step and/or attach increased/decreased importance to certain ROIs based on application-specific criteria.

The feedback in the form of modified ROI definition and/or scores can be used to retrain the corresponding models in the pipeline in an incremental (online) manner leading to improved accuracy across all intermediate steps for new/unseen cases.

Examples of embodiments of the invention are explained in more detail below by means of drawings.

<FIG> displays an embodiment of the method <NUM> for providing a positioning score regarding a positioning of an examining region in an X-ray image according to the invention. The computer-implemented method <NUM> for providing a positioning score regarding a positioning of an examining region in an X-ray image, comprises the steps, preferably in the following order:.

The X-ray image <NUM> is a two-dimensional X-ray image. The first trained function <NUM> is based on an object detection network, preferably the RetinaNet. The second trained function <NUM>, <NUM>,. n and/or the third trained function <NUM> is based on a classifier, preferably the Densenet, or a regression model. The initial convolution layer is modified to focus the network attention based on the heatmap and/or the score-weighted heatmap. The heatmap and/or the at least one region of interest <NUM>, <NUM>,. n is displayed. The user can adjust the heatmap <NUM> and/or the at least one region of interest <NUM>, <NUM>,. A plurality of second trained functions <NUM>, <NUM>,. n can be described as bank of second trained functions <NUM>. The positioning score <NUM> describes a rotation of the examining region. The examining region comprises a knee, the thorax, or a breast.

A modular pipeline for AI-based positioning check is proposed. A radiographic image or an X-ray image <NUM> is used as input. The expected output is the overall score, in particular the positioning score18, indicating the quality of positioning for the given X-ray image <NUM>. The AI-based sub-tasks are encapsulated by three modules:.

The first trained function <NUM> which can be called the ROI Detector uses radiographic image or an X-ray image <NUM> as input, and provides relevant ROIs <NUM>, <NUM>,. n in the X-ray image <NUM> as output. The first trained function <NUM> or the ROI detector detects and identifies all relevant ROIs <NUM>, <NUM>,. n present in the X-ray image <NUM>. This module can be realized using an available object detection network such as the RetinaNet.

The bank of second trained functions <NUM> which can be called a bank of ROI scoring modules uses a radiographic image or an X-ray image <NUM> as well as a ROI heatmap as input and provides individual ROI scores as output.

<FIG> displays an embodiment of the second trained function <NUM>. The second trained functions <NUM> or the ROI scoring modules score each available ROI by focusing attention on the corresponding image region underlying the ROI. This is achieved by using an additional input based on a heatmap <NUM> representation of the ROI. An ROI scoring module can be realized using an available classifier architecture such as the Densenet <NUM> and modifying the initial convolution layer <NUM>, <NUM> to focus the network attention based on the ROI heatmap. The classifier architecture can be replaced with a regression model to increase the scoring sensitivity from a categorical to a continuous range. An individual score <NUM> is provided as output.

<FIG> displays an embodiment of the third trained function <NUM>. The third trained function <NUM> can be called the overall scoring module. The third trained function <NUM> uses a radiographic image or an X-ray image <NUM> as well as a score-weighted ROI heatmap <NUM> as input and provides the final positioning score <NUM> as output.

The overall scoring module computes the final positioning score for the input radiographic image by focusing attention on the image region underlying all available ROIs and their corresponding scores. This is achieved by using an additional input based on an aggregated score-weighted heatmap <NUM> representation of all available ROIs. An overall scoring module can be realized using an available classifier architecture such as the Densenet <NUM> and modifying the initial convolution layer <NUM>, <NUM> to focus the network attention based on the aggregated, score-weighted heatmap <NUM>. The classifier architecture can be replaced with a regression model to increase the scoring sensitivity from a categorical to a continuous range.

<FIG> displays an embodiment of an artificial neural network <NUM>. Alternative terms for "artificial neural network" are "neural network", "artificial neural net" or "neural net".

The artificial neural network <NUM> comprises nodes <NUM>,. , <NUM> and edges <NUM>,. , <NUM>, wherein each edge <NUM>,. , <NUM> is a directed connection from a first node <NUM>,. , <NUM> to a second node <NUM>,. In general, the first node <NUM>,. , <NUM> and the second node <NUM>,. , <NUM> are different nodes <NUM>,. , <NUM>, it is also possible that the first node <NUM>,. , <NUM> and the second node <NUM>,. , <NUM> are identical. For example, in <FIG> the edge <NUM> is a directed connection from the node <NUM> to the node <NUM>, and the edge <NUM> is a directed connection from the node <NUM> to the node <NUM>. An edge <NUM>,. , <NUM> from a first node <NUM>,. , <NUM> to a second node <NUM>,. , <NUM> is also denoted as "ingoing edge" for the second node <NUM>,. , <NUM> and as "outgoing edge" for the first node <NUM>,.

In this embodiment, the nodes <NUM>,. , <NUM> of the artificial neural network <NUM> can be arranged in layers <NUM>,. , <NUM>, wherein the layers can comprise an intrinsic order introduced by the edges <NUM>,. , <NUM> between the nodes <NUM>,. In particular, edges <NUM>,. , <NUM> can exist only between neighboring layers of nodes. In the displayed embodiment, there is an input layer <NUM> comprising only nodes <NUM>,. , <NUM> without an incoming edge, an output layer <NUM> comprising only nodes <NUM>, <NUM> without outgoing edges, and hidden layers <NUM>, <NUM> in-between the input layer <NUM> and the output layer <NUM>. In general, the number of hidden layers <NUM>, <NUM> can be chosen arbitrarily. The number of nodes <NUM>,. , <NUM> within the input layer <NUM> usually relates to the number of input values of the neural network, and the number of nodes <NUM>, <NUM> within the output layer <NUM> usually relates to the number of output values of the neural network.

In particular, a (real) number can be assigned as a value to every node <NUM>,. , <NUM> of the neural network <NUM>. Here, x(n)i denotes the value of the i-th node <NUM>,. , <NUM> of the n-th layer <NUM>,. The values of the nodes <NUM>,. , <NUM> of the input layer <NUM> are equivalent to the input values of the neural network <NUM>, the values of the nodes <NUM>, <NUM> of the output layer <NUM> are equivalent to the output value of the neural network <NUM>. Furthermore, each edge <NUM>,. , <NUM> can comprise a weight being a real number, in particular, the weight is a real number within the interval [-<NUM>, <NUM>] or within the interval [<NUM>, <NUM>]. Here, w(m,n)i,j denotes the weight of the edge between the i-th node <NUM>,. , <NUM> of the m-th layer <NUM>,. , <NUM> and the j-th node <NUM>,. , <NUM> of the n-th layer <NUM>,. Furthermore, the abbreviation w(n)i,j is defined for the weight w(n,n+<NUM>)i,j.

In particular, to calculate the output values of the neural network <NUM>, the input values are propagated through the neural network. In particular, the values of the nodes <NUM>,. , <NUM> of the (n+<NUM>)-th layer <NUM>,. , <NUM> can be calculated based on the values of the nodes <NUM>,. , <NUM> of the n-th layer <NUM>,. , <NUM> by <MAT>.

Herein, the function f is a transfer function (another term is "activation function"). Known transfer functions are step functions, sigmoid function (e.g. the logistic function, the generalized logistic function, the hyperbolic tangent, the Arctangent function, the error function, the smoothstep function) or rectifier functions. The transfer function is mainly used for normalization purposes.

In particular, the values are propagated layer-wise through the neural network, wherein values of the input layer <NUM> are given by the input of the neural network <NUM>, wherein values of the first hidden layer <NUM> can be calculated based on the values of the input layer <NUM> of the neural network, wherein values of the second hidden layer <NUM> can be calculated based in the values of the first hidden layer <NUM>, etc..

In order to set the values w(m,n)i,j for the edges, the neural network <NUM> has to be trained using training data. In particular, training data comprises training input data and training output data (denoted as ti). For a training step, the neural network <NUM> is applied to the training input data to generate calculated output data. In particular, the training data and the calculated output data comprise a number of values, said number being equal with the number of nodes of the output layer.

In particular, a comparison between the calculated output data and the training data is used to recursively adapt the weights within the neural network <NUM> (backpropagation algorithm). In particular, the weights are changed according to <MAT> wherein γ is a learning rate, and the numbers δ(n)j can be recursively calculated as <MAT> based on δ(n+<NUM>)j, if the (n+<NUM>)-th layer is not the output layer, and <MAT> if the (n+<NUM>)-th layer is the output layer <NUM>, wherein f' is the first derivative of the activation function, and y(n+<NUM>)j is the comparison training value for the j-th node of the output layer <NUM>.

<FIG> displays an embodiment of a convolutional neural network <NUM>. In the displayed embodiment, the convolutional neural network comprises <NUM> an input layer <NUM>, a convolutional layer <NUM>, a pooling layer <NUM>, a fully connected layer <NUM> and an output layer <NUM>. Alternatively, the convolutional neural network <NUM> can comprise several convolutional layers <NUM>, several pooling layers <NUM> and several fully connected layers <NUM>, as well as other types of layers. The order of the layers can be chosen arbitrarily, usually fully connected layers <NUM> are used as the last layers before the output layer <NUM>.

In particular, within a convolutional neural network <NUM> the nodes <NUM>,. , <NUM> of one layer <NUM>,. , <NUM> can be considered to be arranged as a d-dimensional matrix or as a d-dimensional image. In particular, in the two-dimensional case the value of the node <NUM>,. , <NUM> indexed with i and j in the n-th layer <NUM>,. , <NUM> can be denoted as x(n)[i,j]. However, the arrangement of the nodes <NUM>,. , <NUM> of one layer <NUM>,. , <NUM> does not have an effect on the calculations executed within the convolutional neural network <NUM> as such, since these are given solely by the structure and the weights of the edges.

In particular, a convolutional layer <NUM> is characterized by the structure and the weights of the incoming edges forming a convolution operation based on a certain number of kernels. In particular, the structure and the weights of the incoming edges are chosen such that the values x(n)k of the nodes <NUM> of the convolutional layer <NUM> are calculated as a convolution x(n)k = Kk * x(n-<NUM>) based on the values x(n-<NUM>) of the nodes <NUM> of the preceding layer <NUM>, where the convolution * is defined in the two-dimensional case as <MAT>.

Here the k-th kernel Kk is a d-dimensional matrix (in this embodiment a two-dimensional matrix), which is usually small compared to the number of nodes <NUM>,. , <NUM> (e.g. a 3x3 matrix, or a 5x5 matrix). In particular, this implies that the weights of the incoming edges are not independent, but chosen such that they produce said convolution equation. In particular, for a kernel being a 3x3 matrix, there are only <NUM> independent weights (each entry of the kernel matrix corresponding to one independent weight), irrespectively of the number of nodes <NUM>,. , <NUM> in the respective layer <NUM>,. In particular, for a convolutional layer <NUM> the number of nodes <NUM> in the convolutional layer is equivalent to the number of nodes <NUM> in the preceding layer <NUM> multiplied with the number of kernels.

If the nodes <NUM> of the preceding layer <NUM> are arranged as a d-dimensional matrix, using a plurality of kernels can be interpreted as adding a further dimension (denoted as "depth" dimension), so that the nodes <NUM> of the convolutional layer <NUM> are arranged as a (d+<NUM>)-dimensional matrix. If the nodes <NUM> of the preceding layer <NUM> are already arranged as a (d+<NUM>)-dimensional matrix comprising a depth dimension, using a plurality of kernels can be interpreted as expanding along the depth dimension, so that the nodes <NUM> of the convolutional layer <NUM> are arranged also as a (d+<NUM>)-dimensional matrix, wherein the size of the (d+<NUM>)-dimensional matrix with respect to the depth dimension is by a factor of the number of kernels larger than in the preceding layer <NUM>.

The advantage of using convolutional layers <NUM> is that spatially local correlation of the input data can exploited by enforcing a local connectivity pattern between nodes of adjacent layers, in particular by each node being connected to only a small region of the nodes of the preceding layer.

In the displayed embodiment, the input layer <NUM> comprises <NUM> nodes <NUM>, arranged as a two-dimensional 6x6 matrix. The convolutional layer <NUM> comprises <NUM> nodes <NUM>, arranged as two two-dimensional 6x6 matrices, each of the two matrices being the result of a convolution of the values of the input layer with a kernel. Equivalently, the nodes <NUM> of the convolutional layer <NUM> can be interpreted as arranges as a three-dimensional 6x6x2 matrix, wherein the last dimension is the depth dimension.

A pooling layer <NUM> can be characterized by the structure and the weights of the incoming edges and the activation function of its nodes <NUM> forming a pooling operation based on a nonlinear pooling function f. For example, in the two dimensional case the values x(n) of the nodes <NUM> of the pooling layer <NUM> can be calculated based on the values x(n-<NUM>) of the nodes <NUM> of the preceding layer <NUM> as <MAT>.

In other words, by using a pooling layer <NUM> the number of nodes <NUM>, <NUM> can be reduced, by replacing a number d<NUM>·d<NUM> of neighboring nodes <NUM> in the preceding layer <NUM> with a single node <NUM> being calculated as a function of the values of said number of neighboring nodes in the pooling layer. In particular, the pooling function f can be the max-function, the average or the L2-Norm. In particular, for a pooling layer <NUM> the weights of the incoming edges are fixed and are not modified by training.

The advantage of using a pooling layer <NUM> is that the number of nodes <NUM>, <NUM> and the number of parameters is reduced. This leads to the amount of computation in the network being reduced and to a control of overfitting.

In the displayed embodiment, the pooling layer <NUM> is a max-pooling, replacing four neighboring nodes with only one node, the value being the maximum of the values of the four neighboring nodes. The max-pooling is applied to each d-dimensional matrix of the previous layer; in this embodiment, the max-pooling is applied to each of the two two-dimensional matrices, reducing the number of nodes from <NUM> to <NUM>.

A fully-connected layer <NUM> can be characterized by the fact that a majority, in particular, all edges between nodes <NUM> of the previous layer <NUM> and the nodes <NUM> of the fully-connected layer <NUM> are present, and wherein the weight of each of the edges can be adjusted individually.

In this embodiment, the nodes <NUM> of the preceding layer <NUM> of the fully-connected layer <NUM> are displayed both as two-dimensional matrices, and additionally as non-related nodes (indicated as a line of nodes, wherein the number of nodes was reduced for a better presentability). In this embodiment, the number of nodes <NUM> in the fully connected layer <NUM> is equal to the number of nodes <NUM> in the preceding layer <NUM>. Alternatively, the number of nodes <NUM>, <NUM> can differ.

Furthermore, in this embodiment the values of the nodes <NUM> of the output layer <NUM> are determined by applying the Softmax function onto the values of the nodes <NUM> of the preceding layer <NUM>. By applying the Softmax function, the sum of the values of all nodes <NUM> of the output layer is <NUM>, and all values of all nodes <NUM> of the output layer are real numbers between <NUM> and <NUM>. In particular, if using the convolutional neural network <NUM> for categorizing input data, the values of the output layer can be interpreted as the probability of the input data falling into one of the different categories.

A convolutional neural network <NUM> can also comprise a ReLU (acronym for "rectified linear units") layer. In particular, the number of nodes and the structure of the nodes contained in a ReLU layer is equivalent to the number of nodes and the structure of the nodes contained in the preceding layer. In particular, the value of each node in the ReLU layer is calculated by applying a rectifying function to the value of the corresponding node of the preceding layer. Examples for rectifying functions are f(x) = max(<NUM>,x), the tangent hyperbolics function or the sigmoid function.

In particular, convolutional neural networks <NUM> can be trained based on the backpropagation algorithm. For preventing overfitting, methods of regularization can be used, e.g. dropout of nodes <NUM>,. , <NUM>, stochastic pooling, use of artificial data, weight decay based on the L1 or the L2 norm, or max norm constraints.

In <FIG>, a mammography system <NUM>, in particular in the form of a tomosynthesis system, is shown by way of example and roughly schematically. Relative directions such as "above", "below", etc. refer to a tomosynthesis system set up for operation as intended. The mammography system <NUM> includes a tomosynthesis device <NUM> and a control device <NUM>. The tomosynthesis device <NUM> has a standing column <NUM> and a source-detector arrangement <NUM>, which in turn comprise an X-ray source <NUM> and an X-ray detector <NUM> with a detector area <NUM>. Standing column <NUM> is in operation on the ground. The source-detector arrangement <NUM> can be connected to it in a movable manner, so that the height of the detector surface <NUM>, i.e. the distance to the substrate, can be adjusted to a chest height of a patient.

A breast O of the patient (shown schematically here) is the object of examination, the breast O, for an examination on the top of the detector surface <NUM>. Above the chest O and the detector surface <NUM> a compression plate <NUM> is arranged, which is movably connected to the source-detector arrangement <NUM>. For the examination, the breast O is compressed and at the same time fixed by lowering the compression plate <NUM> to it, so that pressure is applied on the breast O between compression plate <NUM> and detector surface <NUM>.

The X-ray emitter <NUM> is arranged and designed opposite the X-ray detector <NUM> in such a way that the X-ray detector <NUM> detects X-rays R emitted by it after at least part of the X-ray radiation R has penetrated the patient's breast O. The X-ray emitter <NUM> is relative to the X-ray detector <NUM> by means of a rotary arm <NUM>, for example, in a range of ± <NUM>° around a basic position, in which it is perpendicular to the detector surface <NUM>.

The mammography system <NUM> can in particular a control device <NUM> and a computer unit with a scoring system <NUM> and a training unit <NUM>. The control device <NUM> is connected to a terminal <NUM>, for example having a user interface or display unit, through which a user can communicate commands to the tomosynthesis system <NUM> or retrieve measurement results, for example the X-ray. The control device <NUM> may be located in the same room as the tomosynthesis device <NUM>, but it may also be located in an adjacent control room or at an even greater spatial distance.

<FIG> shows an exemplary embodiment of an X-ray system <NUM>, especially a radiography system, according to the invention. The X-ray system <NUM> has a patient positioning device <NUM> with a table <NUM> fixed to the floor <NUM>. The object <NUM> lies on the table <NUM>. The patient positioning device <NUM> further comprises an X-ray detector unit <NUM>.

The X-ray system <NUM> comprises an X-ray source <NUM> and an X-ray detector unit <NUM>. The X-ray source unit <NUM>, which comprises the X-ray source <NUM> and a collimator unit <NUM>. The X-ray source unit <NUM> can be connected to the ceiling <NUM> of the examination room by means of a ceiling mount <NUM>. By means of the ceiling mount <NUM>, the X-ray source <NUM> can be moved.

The X-ray system <NUM> may also comprise an input unit <NUM> and an output unit <NUM>. The input unit <NUM> and the output unit <NUM> may be connected to the control unit <NUM>. The control unit <NUM> comprises the scoring system <NUM>. The control unit <NUM> may further comprise or be connected to the training unit <NUM>.

Claim 1:
Computer-implemented method (<NUM>) for providing a positioning score regarding a positioning of an examining region in an X-ray image, comprising:
- Receiving input data wherein the input data comprises an X-ray image (<NUM>) comprising the examining region,
- Applying a first trained function (<NUM>) to the input data, wherein at least one region of interest (<NUM>, <NUM>,..., <NUM>.n) is detected in the X-ray image and a heatmap (<NUM>) comprising the at least one region of interest is generated,
- Applying a second trained function (<NUM>, <NUM>, <NUM>, ..., <NUM>.n) to the input data and the heatmap, wherein an individual score for each of the at least one region of interest is generated, and a score-weighted heatmap (<NUM>) is generated based on the at least one region of interest and the individual scores (<NUM>, <NUM>, ..., <NUM>.n),
- Applying a third trained function (<NUM>) to the input data and the score-weighted heatmap, wherein a positioning score is generated,
- Providing the positioning score (<NUM>).