Patent Publication Number: US-2021182674-A1

Title: Automatic training and deployment of deep learning technologies

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 62/947,248, filed Dec. 12, 2019, the disclosure of which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present invention relates generally to deep learning technologies, and in particular to automatic training and deployment of deep learning technologies to improve model performance. 
     BACKGROUND 
     Deep learning models have been utilized for performing various medical imaging analysis tasks, such as, e.g., cancer detection and organ segmentation. Such deep learning models are trained with a large amount of training data collected from different clinical locations, fulfillment centers, and other sources. Conventionally, the training workflow for training deep learning models is manually performed by a scientist. However, the manual training of deep learning models is time consuming, taking away time from the scientist that can otherwise be used for performing clinical research and other important tasks. Additionally, the manual training of deep learning models makes it difficult to regularly retrain the deep learning models with new training data which would improve model performance. 
     BRIEF SUMMARY OF THE INVENTION 
     In accordance with one or more embodiments, systems and methods for automatically training a machine learning based model are provided. A trigger for automatically training a machine learning based model is received. In response to receiving the trigger, a preprocessing manager for executing preprocessing code for preprocessing training data is automatically invoked. A training manager for executing training code for training the machine learning based model based on the preprocessed training data is automatically invoked. A deployment manager for executing deployment code for converting the trained machine learning based model to a production model is automatically invoked. The production model is output. In one embodiment, the machine learning based model is a deep learning model. 
     In one embodiment, the trigger for automatically training a machine learning based model is received in response to a user request or at a predetermined time. 
     In one embodiment, the steps of receiving, automatically invoking the preprocessing manager, automatically invoking the training manager, and automatically invoking the deployment manager are performed by a main manager. The main manager, the preprocessing manager, the training manager, and the deployment manager are implemented in separate nodes of a computing device. 
     In one embodiment, the preprocessing code is further for generating a preprocessing report comprising database descriptors and statistics for the training data and validation data, the training code is further for generating a training report comprising a log of training data, and the deployment code is further for generating a conversion report comprising data comparing performance of the trained machine learning based model and the production model and a performance report comprising an evaluation of the performance of the production model. 
     These and other advantages of the invention will be apparent to those of ordinary skill in the art by reference to the following detailed description and the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an illustrative system architecture for automatically training a machine learning based model, in accordance with one or more embodiments; 
         FIG. 2  shows a workflow for automatically training a machine learning based model, in accordance with one or more embodiments; 
         FIG. 3  shows a method for automatically training a machine learning based model, in accordance with one or more embodiments; 
         FIG. 4  shows an exemplary artificial neural network that may be used to implement one or more embodiments; 
         FIG. 5  shows a convolutional neural network that may be used to implement one or more embodiments; and 
         FIG. 6  shows a high-level block diagram of a computer that may be used to implement one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention generally relates to methods and systems for automatic training and deployment of deep learning technologies. Embodiments of the present invention are described herein to give a visual understanding of such methods and systems. A digital image is often composed of digital representations of one or more objects (or shapes). The digital representation of an object is often described herein in terms of identifying and manipulating the objects. Such manipulations are virtual manipulations accomplished in the memory or other circuitry/hardware of a computer system. Accordingly, is to be understood that embodiments of the present invention may be performed within a computer system using data stored within the computer system. Embodiments described herein are described with reference to the drawings, where like reference numerals represent the same or similar elements. 
     Embodiments described herein provide for methods and systems for automating the training workflow of deep learning models and other machine learning based models. To facilitate the automatic training of deep learning networks, the training workflow is decomposed into three stages: preprocessing, training, and deployment. Accordingly, a main manager is configured to orchestrate the automatic invocation of a preprocessing manager for preprocessing training data, a training manager for training deep learning models based on the preprocessed training data, and a deployment manager for converting the trained deep learning model into a production model for use in a clinical site. Advantageously, by automating the training workflow, embodiments described herein enable scientists to allocate their time for performing clinical research projects and other important tasks instead of manually managing the training workflow for deep learning models. In addition, embodiments described herein enable automatic training or retraining of deep learning models at periodic intervals using newly available training data to thereby improve model performance. 
       FIG. 1  shows an illustrative system architecture  100  for automatically training a machine learning based model, in accordance with one or more embodiments. As shown in system architecture  100 , a main manager  104  is configured to orchestrate the automatic invocation of a preprocessing manager  106 , a training manager  112 , and a deployment manager  118  for automatic training of machine learning based models. Main manager  104 , preprocessing manager  106 , training manager  112 , and deployment manager  118  are respectively implemented by nodes  134 ,  136 ,  138 , and  140 . Each node  134 ,  136 ,  138 , and  140  may have different specifications to meet performance requirements of each manager. In one embodiment, nodes  134 ,  136 ,  138 , and  140  are CPU (central processing unit) or GPU (graphics processing unit) nodes of a supercomputer. In one embodiment, nodes  134 ,  136 ,  138 , and  140  (and other resources) may be allocated in accordance with a configuration file written by a user (e.g., scientist) to respectively implement main manager  104 , preprocessing manager  106 , training manager  112 , and deployment manager  118 .  FIG. 1  will be further described below in connection with  FIGS. 2 and 3 . 
       FIG. 2  shows a workflow  200  for automatically training a machine learning based model, in accordance with one or more embodiments. In one example, various elements of workflow  200  are implemented by one or more suitable computing devices (e.g., a supercomputer) in accordance with system architecture  100  of  FIG. 1 .  FIG. 3  shows a method  300  for automatically training a machine learning based model, in accordance with one or more embodiments.  FIG. 2  and  FIG. 3  will be discussed together, with continued reference to  FIG. 1 . The steps of method  300  of  FIG. 3  may be performed by one or more suitable computing devices, such as, e.g., computer  602  of  FIG. 6 . In one embodiment, the steps of method  300  are performed by main manager  104  of  FIGS. 1 and 2 . 
     At step  302 , a trigger for automatically training a machine learning based model is received. The machine learning based model may be a deep learning model or any suitable machine learning based model for performing a medical image analysis task, such as, e.g., detection, segmentation, etc. The trigger may be any suitable trigger for training the machine learning based model. In one embodiment, the trigger is received in response to a user request. In another embodiment, the trigger is received at predefined times or at predefined time intervals. In another embodiment, the trigger is received in response to a certain event, such as, e.g., the collection of a particular amount of new training data. 
     At step  304 , in response to receiving the trigger, a preprocessing manager for executing preprocessing code for preprocessing training data is automatically invoked. In one example, the preprocessing manager may be preprocessing manager  106  of  FIGS. 1 and 2 . As shown in  FIGS. 1 and 2 , preprocessing manager  106  runs or executes preprocessing code  110  via adaptor  108 . Adaptor  108  is an interface between preprocessing manager  106  and preprocessing code  110 . Preprocessing code  110  comprises computer program instructions written by a user (e.g., a scientist) defining how training data is to be preprocessed. For example, preprocessing code  110  may comprise computer program instructions for augmenting training data (e.g., by artificially enriching the dataset with images derived from the images), formatting the training data into a particular training format used at training (e.g., by cropping and resampling the training data to a particular resolution and saving the images into a specific format that may differ from the one used in the training data database), splitting the training data into training/validation datasets, moving the training data to dedicated locations required by training, and generating renderings for review. 
     Preprocessing code  110  is also configured to generate a preprocessing report  126  comprising database keys (e.g., identifiers) for the training data. Database keys are key information on the data used for training and preprocess, such as, e.g., the data keys in the training and validations splits or other descriptors that reflect the distribution of the data used. Preprocessing report  126  may comprise any statistics representing the data distribution of the training and validation dataset, any statistic derived from image metadata representing the image acquisition parameters, the image content, demographics, run time, image renderings, etc. 
     The training data comprises training images and corresponding annotations stored in training data database  102 . The training images may be of any suitable modality, such as, e.g., MRI (magnetic resonance imaging), CT (computed tomography), x-ray, US (ultrasound), or any other modality or combination of modalities. The training images may comprise 2D (two dimensional) images or 3D (three dimensional) volumes, and may each comprise a single image or a plurality of images (e.g., a sequence of images acquired over time). The training images may be received directly from an image acquisition device as the images are acquired and stored in training data database  102 , or can be received by loading previously acquired images from a storage or memory of a computer system or receiving the images from a remote computer system and stored in training data database  102 . 
     Once executed, preprocessing code  110  outputs the path to the preprocessed training data and preprocessing report  126 . Preprocessing manager  106  then returns an indication that the preprocessing of the training data has been completed to main manager  104 . 
     At step  306 , a training manager for executing training code for training the machine learning based model based on the preprocessed training data is automatically invoked. The training manager may be automatically invoked in response to receiving the indication, from preprocessing manager  106 , that the preprocessing of the training data has been completed. In one example, the training manager may be training manager  112  of  FIGS. 1 and 2 . As shown in  FIGS. 1 and 2 , training manager  112  runs or executes training code  116  via adaptor  114 . Adaptor  114  is an interface between training manager  112  and training code  116 . Training code  116  comprises computer program instructions written by a user defining how the machine learning based model is to be trained. For example, training code  116  may comprise computer program instructions for defining the machine learning based model, weights and annotations, optimization functions, for saving intermediate models, etc. Training code  116  is also configured to generate a training report  128  comprising a log of training data, such as, e.g., accuracy, validation loss, training and validation curves (loss and other key performance indicators), performance at each epoch, run time per epoch and total run time, network visualizations, etc. 
     Once executed, training code  116  outputs the path to the trained machine learning based model and training report  128 . Training manager  112  then returns an indication that the training of the machine learning based model has been completed and the path to the trained machine learning based model to main manager  104 . 
     At step  308 , a deployment manger for executing code for converting the trained machine learning based model to a production model is automatically invoked. The deployment manager may be automatically invoked in response to receiving the indication, from training manager  112 , that the training of the machine learning based model has been completed. In one example, the deployment manager may be deployment manager  118  of  FIGS. 1 and 2 . As shown in  FIGS. 1 and 2 , deployment manager  118  runs or executes deployment code  122 . Deployment code  122  comprises computer program instructions written by a user defining how the trained machine learning model is to be converted to production model  124 . The trained machine learning based model is implemented with a framework or format suitable for training and development, but may not be suitable for a production environment. One example of a production environment is a clinical site where machine learning based models must be applied with speed, accuracy, and reliability. Deployment code  122  converts the trained machine learning based model to production model  124  implemented with a framework or format suitable for a production environment. Deployment code  122  is dependent on the training framework and the target production framework. In one embodiment, production model  124  is implemented with a framework or format optimized for the production environment, such that production model  124  is faster, more reliable, etc. as compared to the trained machine learning based model. Deployment code  122  is also configured to generate a conversion report  130  and a performance report  132 . Conversion report  130  comprises data comparing the performance of the trained machine learning based model and the production model. For example, conversion report  130  may comprise comparisons on random images and real images to make sure inferences on both networks are producing the same results, and gains in key performance indicators for the production model  124  (e.g., run time, memory requirements, model size, etc.). Performance report  132  comprises an evaluation of the performance of the production model (e.g., based on user defined metrics). For example, performance report  132  may compute the performance of production model  124  on a validation set, compare predictions with ground truth data and baseline versions, and generate renderings for predictions on the validation data set. 
     Once executed, deployment code  122  outputs paths to production model  124  along with conversion report  130  and performance report  132 . Deployment manager  118  then returns an indication that the conversion of the trained machine learning based model to production model  124  has been completed and a path to production model  124  to main manager  104 . 
     At step  310 , production model  124  is output. In some embodiments, preprocessing report  126 , training report  128 , conversion report  130 , and/or performance report  132  are also output. Production model  124 , preprocessing report  126 , training report  128 , conversion report  130 , and/or performance report  132  may be output in response to receiving the indication, from deployment manager  112 , that the conversion of the trained machine learning based model to the production model has been completed. Production model  124 , preprocessing report  126 , training report  128 , conversion report  130 , and/or performance report  132  may be output by storing production model  124 , preprocessing report  126 , training report  128 , conversion report  130 , and/or performance report  132  on a memory or storage of a computer system, or by transmitting production model  124 , preprocessing report  126 , training report  128 , conversion report  130 , and/or performance report  132  to a remote computer system. In some embodiments, the preprocessing report  126 , training report  128 , conversion report  130 , and/or performance report  132  may be output by displaying preprocessing report  126 , training report  128 , conversion report  130 , and/or performance report  132  on a display device of a computer system. 
     Advantageously, the automatic training of machine learning based models in accordance with method  300  enables frequent training and retraining of machine learning based models for performing medical imaging analysis tasks, thereby improving model performance. By decomposing the training workflow for training machine learning based models into a preprocessing stage, a training stage, and a deployment stage, the system architecture (e.g., system architecture  100  of  FIG. 1 ) implementing method  300  may be modularly defined, enabling a user to easily input their code (e.g., preprocessing code  110 , training code  116 , and/or deployment code  122 ) to implement automatic training of machine learning based models in accordance with method  300 . Further, the decomposition of the training workflow enables a report to be automatically generated for each step, allowing the user to better understand the entire training workflow from training data collection to generation of the production model. Such reports may additionally be utilized for submission to regulatory agencies. 
     Embodiments described herein are described with respect to the claimed 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 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 providing system. 
     In general, a trained machine learning based network mimics cognitive functions that humans associate with other human minds. In particular, by training based on training data, the trained machine learning based network is able to adapt to new circumstances and to detect and extrapolate patterns. 
     In general, parameters of a machine learning based network 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 machine learning based network can be adapted iteratively by several steps of training. 
     In particular, a trained machine learning based network can comprise a neural network, a support vector machine, a decision tree, and/or a Bayesian network, and/or the trained machine learning based network 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. 
       FIG. 4  shows an embodiment of an artificial neural network  400 , in accordance with one or more embodiments. Alternative terms for “artificial neural network” are “neural network”, “artificial neural net” or “neural net”. Machine learning networks described herein, such as, e.g., the machine learning based model automatically trained according to workflow  200  of  FIG. 2  or method  300  of  FIG. 3 , may be implemented using artificial neural network  400 . 
     The artificial neural network  400  comprises nodes  402 - 422  and edges  432 ,  434 , . . . ,  436 , wherein each edge  432 ,  434 , . . . ,  436  is a directed connection from a first node  402 - 422  to a second node  402 - 422 . In general, the first node  402 - 422  and the second node  402 - 422  are different nodes  402 - 422 , it is also possible that the first node  402 - 422  and the second node  402 - 422  are identical. For example, in  FIG. 4 , the edge  432  is a directed connection from the node  402  to the node  406 , and the edge  434  is a directed connection from the node  404  to the node  406 . An edge  432 ,  434 , . . . ,  436  from a first node  402 - 422  to a second node  402 - 422  is also denoted as “ingoing edge” for the second node  402 - 422  and as “outgoing edge” for the first node  402 - 422 . 
     In this embodiment, the nodes  402 - 422  of the artificial neural network  400  can be arranged in layers  424 - 430 , wherein the layers can comprise an intrinsic order introduced by the edges  432 ,  434 , . . . ,  436  between the nodes  402 - 422 . In particular, edges  432 ,  434 , . . . ,  436  can exist only between neighboring layers of nodes. In the embodiment shown in  FIG. 4 , there is an input layer  424  comprising only nodes  402  and  404  without an incoming edge, an output layer  430  comprising only node  422  without outgoing edges, and hidden layers  426 ,  428  in-between the input layer  424  and the output layer  430 . In general, the number of hidden layers  426 ,  428  can be chosen arbitrarily. The number of nodes  402  and  404  within the input layer  424  usually relates to the number of input values of the neural network  400 , and the number of nodes  422  within the output layer  430  usually relates to the number of output values of the neural network  400 . 
     In particular, a (real) number can be assigned as a value to every node  402 - 422  of the neural network  400 . Here, x (n)   i  denotes the value of the i-th node  402 - 422  of the n-th layer  424 - 430 . The values of the nodes  402 - 422  of the input layer  424  are equivalent to the input values of the neural network  400 , the value of the node  422  of the output layer  430  is equivalent to the output value of the neural network  400 . Furthermore, each edge  432 ,  434 , . . . ,  436  can comprise a weight being a real number, in particular, the weight is a real number within the interval [−1, 1] or within the interval [0, 1]. Here, w (m,n)   i,j  denotes the weight of the edge between the i-th node  402 - 422  of the m-th layer  424 - 430  and the j-th node  402 - 422  of the n-th layer  424 - 430 . Furthermore, the abbreviation w (n)   i,j  is defined for the weight w (n,n+1)   i,j . 
     In particular, to calculate the output values of the neural network  400 , the input values are propagated through the neural network. In particular, the values of the nodes  402 - 422  of the (n+1)-th layer  424 - 430  can be calculated based on the values of the nodes  402 - 422  of the n-th layer  424 - 430  by 
         x   j   (n+1)   f (Σ i   x   i   (n)   ·w   i,j   (n) ).
 
     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  424  are given by the input of the neural network  400 , wherein values of the first hidden layer  426  can be calculated based on the values of the input layer  424  of the neural network, wherein values of the second hidden layer  428  can be calculated based in the values of the first hidden layer  426 , etc. 
     In order to set the values w (m,n)   i,j  for the edges, the neural network  400  has to be trained using training data. In particular, training data comprises training input data and training output data (denoted as t i ). For a training step, the neural network  400  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  400  (backpropagation algorithm). In particular, the weights are changed according to 
         w′   i,j   (n)   =w   i,j   (n) ·γ·δ j   (n)   ·x   i   (n)  
 
     wherein γ is a learning rate, and the numbers δ (n)   j  can be recursively calculated as 
       δ j   (n) =(Σ k δ k   (n+1)   ·w   j,k   (n+1) )· f′ (Σ i   x   i   (n)   ·w   i,j   (n) )
 
     based on δ (n+1)   j , if the (n+1)-th layer is not the output layer, and 
       δ j   (n) =( x   k   (n+1)   −t   j   (n+1) )· f ′(Σ i   x   i   (n)   ·w   i,j   (n) )
 
     if the (n+1)-th layer is the output layer  430 , wherein f′ is the first derivative of the activation function, and y (n+1)   j  is the comparison training value for the j-th node of the output layer  430 . 
       FIG. 5  shows a convolutional neural network  500 , in accordance with one or more embodiments. Machine learning networks described herein, such as, e.g., the machine learning based model automatically trained according to workflow  200  of  FIG. 2  or method  300  of  FIG. 3 , may be implemented using convolutional neural network  500 . 
     In the embodiment shown in  FIG. 5 , the convolutional neural network comprises  500  an input layer  502 , a convolutional layer  504 , a pooling layer  506 , a fully connected layer  508 , and an output layer  510 . Alternatively, the convolutional neural network  500  can comprise several convolutional layers  504 , several pooling layers  506 , and several fully connected layers  508 , as well as other types of layers. The order of the layers can be chosen arbitrarily, usually fully connected layers  508  are used as the last layers before the output layer  510 . 
     In particular, within a convolutional neural network  500 , the nodes  512 - 520  of one layer  502 - 510  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  512 - 520  indexed with i and j in the n-th layer  502 - 510  can be denoted as x (n   )[i,j] . However, the arrangement of the nodes  512 - 520  of one layer  502 - 510  does not have an effect on the calculations executed within the convolutional neural network  500  as such, since these are given solely by the structure and the weights of the edges. 
     In particular, a convolutional layer  504  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  514  of the convolutional layer  504  are calculated as a convolution x (n)   k =K k *x (n−1)  based on the values x (n−1)  of the nodes  512  of the preceding layer  502 , where the convolution*is defined in the two-dimensional case as 
         x   k   (n)   [i,j] =( K   k   *x   (n−1) ) [i,j]=Σ i′ Σ j′   K   k   [i′,j′]·x   (n−1)   [i−i′, j−j′].  
 
     Here the k-th kernel K k  is a d-dimensional matrix (in this embodiment a two-dimensional matrix), which is usually small compared to the number of nodes  512 - 518  (e.g. a 3×3 matrix, or a 5×5 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 3×3 matrix, there are only 9 independent weights (each entry of the kernel matrix corresponding to one independent weight), irrespectively of the number of nodes  512 - 520  in the respective layer  502 - 510 . In particular, for a convolutional layer  504 , the number of nodes  514  in the convolutional layer is equivalent to the number of nodes  512  in the preceding layer  502  multiplied with the number of kernels. 
     If the nodes  512  of the preceding layer  502  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  514  of the convolutional layer  504  are arranged as a (d+1)-dimensional matrix. If the nodes  512  of the preceding layer  502  are already arranged as a (d+1)-dimensional matrix comprising a depth dimension, using a plurality of kernels can be interpreted as expanding along the depth dimension, so that the nodes  514  of the convolutional layer  504  are arranged also as a (d+1)-dimensional matrix, wherein the size of the (d+1)-dimensional matrix with respect to the depth dimension is by a factor of the number of kernels larger than in the preceding layer  502 . 
     The advantage of using convolutional layers  504  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 embodiment shown in  FIG. 5 , the input layer  502  comprises  36  nodes  512 , arranged as a two-dimensional 6×6 matrix. The convolutional layer  504  comprises  72  nodes  514 , arranged as two two-dimensional 6×6 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  514  of the convolutional layer  504  can be interpreted as arranges as a three-dimensional 6×6×2 matrix, wherein the last dimension is the depth dimension. 
     A pooling layer  506  can be characterized by the structure and the weights of the incoming edges and the activation function of its nodes  516  forming a pooling operation based on a non-linear pooling function f. For example, in the two dimensional case the values x (n)  of the nodes  516  of the pooling layer  506  can be calculated based on the values x (n−1)  of the nodes  514  of the preceding layer  504  as 
         x   (n)   [i,j]=f ( x   (n−1)   [id   1   , jd   2   ], . . . , x   (n−1)   [id   1   +d   1 −1,  jd   2   +d   2 −1])
 
     In other words, by using a pooling layer  506 , the number of nodes  514 ,  516  can be reduced, by replacing a number d1·d2 of neighboring nodes  514  in the preceding layer  504  with a single node  516  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  506  the weights of the incoming edges are fixed and are not modified by training. 
     The advantage of using a pooling layer  506  is that the number of nodes  514 ,  516  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 embodiment shown in  FIG. 5 , the pooling layer  506  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 72 to 18. 
     A fully-connected layer  508  can be characterized by the fact that a majority, in particular, all edges between nodes  516  of the previous layer  506  and the nodes  518  of the fully-connected layer  508  are present, and wherein the weight of each of the edges can be adjusted individually. 
     In this embodiment, the nodes  516  of the preceding layer  506  of the fully-connected layer  508  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  518  in the fully connected layer  508  is equal to the number of nodes  516  in the preceding layer  506 . Alternatively, the number of nodes  516 ,  518  can differ. 
     Furthermore, in this embodiment, the values of the nodes  520  of the output layer  510  are determined by applying the Softmax function onto the values of the nodes  518  of the preceding layer  508 . By applying the Softmax function, the sum the values of all nodes  520  of the output layer  510  is 1, and all values of all nodes  520  of the output layer are real numbers between 0 and 1. 
     A convolutional neural network  500  can also comprise a ReLU (rectified linear units) layer or activation layers with non-linear transfer functions. 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. 
     The input and output of different convolutional neural network blocks can be wired using summation (residual/dense neural networks), element-wise multiplication (attention) or other differentiable operators. Therefore, the convolutional neural network architecture can be nested rather than being sequential if the whole pipeline is differentiable. 
     In particular, convolutional neural networks  500  can be trained based on the backpropagation algorithm. For preventing overfitting, methods of regularization can be used, e.g. dropout of nodes  512 - 520 , stochastic pooling, use of artificial data, weight decay based on the L1 or the L2 norm, or max norm constraints. Different loss functions can be combined for training the same neural network to reflect the joint training objectives. A subset of the neural network parameters can be excluded from optimization to retain the weights pretrained on another datasets. 
     Systems, apparatuses, and methods described herein may be implemented using digital circuitry, or using one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components. Typically, a computer includes a processor for executing instructions and one or more memories for storing instructions and data. A computer may also include, or be coupled to, one or more mass storage devices, such as one or more magnetic disks, internal hard disks and removable disks, magneto-optical disks, optical disks, etc. 
     Systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computers are located remotely from the server computer and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers. 
     Systems, apparatus, and methods described herein may be implemented within a network-based cloud computing system. In such a network-based cloud computing system, a server or another processor that is connected to a network communicates with one or more client computers via a network. A client computer may communicate with the server via a network browser application residing and operating on the client computer, for example. A client computer may store data on the server and access the data via the network. A client computer may transmit requests for data, or requests for online services, to the server via the network. The server may perform requested services and provide data to the client computer(s). The server may also transmit data adapted to cause a client computer to perform a specified function, e.g., to perform a calculation, to display specified data on a screen, etc. For example, the server may transmit a request adapted to cause a client computer to perform one or more of the steps or functions of the methods and workflows described herein, including one or more of the steps or functions of  FIGS. 2 and 3 . Certain steps or functions of the methods and workflows described herein, including one or more of the steps or functions of  FIGS. 2 and 3 , may be performed by a server or by another processor in a network-based cloud-computing system. Certain steps or functions of the methods and workflows described herein, including one or more of the steps of  FIGS. 2 and 3 , may be performed by a client computer in a network-based cloud computing system. The steps or functions of the methods and workflows described herein, including one or more of the steps of  FIGS. 2 and 3 , may be performed by a server and/or by a client computer in a network-based cloud computing system, in any combination. 
     Systems, apparatus, and methods described herein may be implemented using a computer program product tangibly embodied in an information carrier, e.g., in a non-transitory machine-readable storage device, for execution by a programmable processor; and the method and workflow steps described herein, including one or more of the steps or functions of  FIGS. 2 and 3 , may be implemented using one or more computer programs that are executable by such a processor. A computer program is a set of computer program instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. 
     A high-level block diagram of an example computer  602  that may be used to implement systems, apparatus, and methods described herein is depicted in  FIG. 6 . Computer  602  includes a processor  604  operatively coupled to a data storage device  612  and a memory  610 . Processor  604  controls the overall operation of computer  602  by executing computer program instructions that define such operations. The computer program instructions may be stored in data storage device  612 , or other computer readable medium, and loaded into memory  610  when execution of the computer program instructions is desired. Thus, the method and workflow steps or functions of  FIG. 2  can be defined by the computer program instructions stored in memory  610  and/or data storage device  612  and controlled by processor  604  executing the computer program instructions. For example, the computer program instructions can be implemented as computer executable code programmed by one skilled in the art to perform the method and workflow steps or functions of  FIGS. 2 and 3 . Accordingly, by executing the computer program instructions, the processor  604  executes the method and workflow steps or functions of  FIGS. 2 and 3 . Computer  602  may also include one or more network interfaces  606  for communicating with other devices via a network. Computer  602  may also include one or more input/output devices  608  that enable user interaction with computer  602  (e.g., display, keyboard, mouse, speakers, buttons, etc.). 
     Processor  604  may include both general and special purpose microprocessors, and may be the sole processor or one of multiple processors of computer  602 . Processor  604  may include one or more central processing units (CPUs), for example. Processor  604 , data storage device  612 , and/or memory  610  may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs) and/or one or more field programmable gate arrays (FPGAs). 
     Data storage device  612  and memory  610  each include a tangible non-transitory computer readable storage medium. Data storage device  612 , and memory  610 , may each include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices such as internal hard disks and removable disks, magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (DVD-ROM) disks, or other non-volatile solid state storage devices. 
     Input/output devices  608  may include peripherals, such as a printer, scanner, display screen, etc. For example, input/output devices  608  may include a display device such as a cathode ray tube (CRT) or liquid crystal display (LCD) monitor for displaying information to the user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to computer  602 . 
     An image acquisition device  614  can be connected to the computer  602  to input image data (e.g., medical images) to the computer  602 . It is possible to implement the image acquisition device  614  and the computer  602  as one device. It is also possible that the image acquisition device  614  and the computer  602  communicate wirelessly through a network. In a possible embodiment, the computer  602  can be located remotely with respect to the image acquisition device  614 . 
     Any or all of the systems and apparatus discussed herein may be implemented using one or more computers such as computer  602 . 
     One skilled in the art will recognize that an implementation of an actual computer or computer system may have other structures and may contain other components as well, and that  FIG. 6  is a high-level representation of some of the components of such a computer for illustrative purposes. 
     The foregoing Detailed Description is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the Detailed Description, but rather from the claims as interpreted according to the full breadth permitted by the patent laws. It is to be understood that the embodiments shown and described herein are only illustrative of the principles of the present invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Those skilled in the art could implement various other feature combinations without departing from the scope and spirit of the invention.