Patent Publication Number: US-2023162487-A1

Title: System and method for deep learning techniques utilizing continuous federated learning with a distributed data generative model

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation-in-part Application of U.S. Pat. Application Serial No. 17/365,650, entitled “SYSTEM AND METHOD FOR DEEP LEARNING TECHNIQUES UTILIZING CONTINUOUS FEDERATED LEARNING WITH A DISTRIBUTED DATA GENERATIVE MODEL”, filed Jul. 1, 2021, which is herein incorporated. 
    
    
     BACKGROUND 
     The subject matter disclosed herein relates to deep learning techniques and, more particularly, to systems and methods for deep learning techniques utilizing continuous federated learning with a distributed data generative model. 
     Deep learning models have been proven successful in addressing problems involving sufficiently large, balanced and labeled datasets that appear in computer vision, speech processing, image processing, and other problems. Ideally, it is desired that these models continuously learn and adapt with new data, but this remains a challenge for neural network models since most of these models are trained with static large batches of data. Retraining with incremental data generally leads to catastrophic forgetting (i.e. training a model with new information interferes with previously learned knowledge). 
     Ideally, artificial intelligence (AI) learning systems should adapt and learn continuously with new knowledge while refining existing knowledge. Current AI learning schemes assume that all samples are available during the training phase and, therefore, requires retraining of the network parameters on the entire dataset in order to adapt to changes in the data distribution. Although retraining from scratch pragmatically addresses catastrophic forgetting, in many practical scenarios, data privacy concerns do not allow for sharing of training data. In those cases, retraining with incremental new data can lead to significant loss of accuracy (catastrophic forgetting). 
     In addition, in the medical imaging domain (no matter how large the dataset), balanced and manually labelled datasets capturing all variabilities are rare. In presence of datasets that significantly differ from the training data in terms of appearance/contrast, shape (of organs/structures of interest) and field of view, model performance may be adversely affected. Current deep learning schemes assume that all samples are available during the training phase and, therefore, require retraining of the network parameters on the entire dataset in order to adapt to changes in the data distribution. However, such retraining is expensive as it involves a large amount of data acquisition and manual annotations for ground truth generation for those data. 
     BRIEF DESCRIPTION 
     A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below. 
     In one embodiment, a computer implemented method is provided. The method includes establishing, via multiple processors, a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites. The method also includes retraining or retuning, via the multiple processors, the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets. 
     In another embodiment, a deep learning-based continuous federated learning network system is provided. The system includes a global site including a global model. The system also includes multiple local sites, wherein each respective local site of the multiple local sites includes a respective local model derived from the global model. The system further includes multiple processors configured to retrain or retune the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets. 
     In a further embodiment, a non-transitory computer-readable medium, the computer-readable medium including processor-executable code that when executed by one or more processors, causes the one or more processors to perform actions. The actions include establish a continuous federated learning framework comprising a global model at a global site and respective local models derived from the global model at respective local sites. The actions also include retraining or retuning the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets or models generated from the actual datasets. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
         FIG.  1    is an embodiment of a schematic diagram of a continuous federated learning scheme or scenario, in accordance with aspects of the present disclosure; 
         FIG.  2    is an embodiment of a schematic diagram of a continuous federated learning scheme or scenario (e.g., utilizing distributed data generative models), in accordance with aspects of the present disclosure; 
         FIG.  3    is an embodiment of a schematic diagram of a centralized arrangement for a global site and local sites, in accordance with aspects of the present disclosure; 
         FIG.  4    is an embodiment of a schematic diagram of a decentralized arrangement for a global site and local sites, in accordance with aspects of the present disclosure; 
         FIG.  5    is a block diagram of a processor-based device or system that may be configured to implement functionality described herein, in accordance with aspects of the present disclosure; 
         FIG.  6    is an embodiment of a flow chart of a method for retraining local and global models, in accordance with aspects of the present disclosure; 
         FIG.  7    is an embodiment of a flow chart of a method for retraining local and global models, in accordance with aspects of the present disclosure; 
         FIG.  8    illustrate examples of synthetic medical images (e.g., FLAIR MRI images) generated with a generative model, in accordance with aspects of the present disclosure; 
         FIG.  9    illustrate examples of synthetic medical images (e.g., T2 MRI images) generated with a generative model, in accordance with aspects of the present disclosure; 
         FIG.  10    is an embodiment of a schematic diagram of utilization of a trained generative model for guided tissue contrast transformation between pediatric vertebrae magnetic resonance images and adult vertebrae magnetic resonance images, in accordance with aspects of the present disclosure; 
         FIG.  11    is an embodiment of a flow chart of a method for utilizing a trained generative model for guided tissue contrast transformation between pediatric vertebrae magnetic resonance images and adult vertebrae magnetic resonance images, in accordance with aspects of the present disclosure; 
         FIG.  12    is an embodiment of a flow chart of a method for training a generative model for guided tissue contrast transformation between pediatric vertebrae magnetic resonance images and adult vertebrae magnetic resonance images, in accordance with aspects of the present disclosure; 
         FIG.  13    illustrates examples of predictions from segmentation on original T2-weighted magnetic resonance images and transformed T2-weighted magnetic resonance images, in accordance with aspects of the present disclosure; 
         FIG.  14    illustrates an example of an un-paired dataset utilized for training a generative model for guided tissue contrast transformation between pediatric vertebrae magnetic resonance images and adult vertebrae magnetic resonance images, in accordance with aspects of the present disclosure; 
         FIG.  15    illustrates the application of guided tissue contrast transformation to a pediatric vertebrae magnetic resonance image, in accordance with aspects of the present disclosure; and 
         FIG.  16    illustrates different tissue decomposition images derived from adult and pediatric T2-weighted images for regression training, in accordance with aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     One or more specific embodiments will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers’ specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure. 
     When introducing elements of various embodiments of the present invention, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Furthermore, any numerical examples in the following discussion are intended to be nonlimiting, and thus additional numerical values, ranges, and percentages are within the scope of the disclosed embodiments. 
     Some generalized information is provided to provide both general context for aspects of the present disclosure and to facilitate understanding and explanation of certain of the technical concepts described herein. 
     Deep-learning (DL) approaches discussed herein may be based on artificial neural networks, and may therefore encompass one or more of deep neural networks, fully connected networks, convolutional neural networks (CNNs), perceptrons, encoders-decoders, recurrent networks, wavelet filter banks, u-nets, generative adversarial networks (GANs), or other neural network architectures. The neural networks may include shortcuts, activations, batch-normalization layers, and/or other features. These techniques are referred to herein as deep-learning techniques, though this terminology may also be used specifically in reference to the use of deep neural networks, which is a neural network having a plurality of layers. 
     As discussed herein, deep-learning techniques (which may also be known as deep machine learning, hierarchical learning, or deep structured learning) are a branch of machine learning techniques that employ mathematical representations of data and artificial neural networks for learning and processing such representations. By way of example, deep-learning approaches may be characterized by their use of one or more algorithms to extract or model high level abstractions of a type of data-of-interest. This may be accomplished using one or more processing layers, with each layer typically corresponding to a different level of abstraction and, therefore potentially employing or utilizing different aspects of the initial data or outputs of a preceding layer (i.e., a hierarchy or cascade of layers) as the target of the processes or algorithms of a given layer. In an image processing or reconstruction context, this may be characterized as different layers corresponding to the different feature levels or resolution in the data. In general, the processing from one representation space to the next-level representation space can be considered as one ‘stage’ of the process. Each stage of the process can be performed by separate neural networks or by different parts of one larger neural network. 
     Deep neural nets combine feature representation learning and classifiers in a unified framework and have proven successful in many a problem involving sufficiently large, balanced and labeled datasets that appear in computer vision, speech processing, and image processing, and other problems. However, problems related to healthcare or inflight monitoring offer a different set of challenges like limited data, diversity in sample distributions, and limited or no access to training data. Transfer learning is a common framework to retrain models given new incoming data but these set of models suffer from catastrophic forgetting (i.e., catastrophic loss of previously learned responses, whenever an attempt is made to train the network with a single new (additional) response). The challenge is to learn and adapt with new incoming data, while retaining memory of previously learned responses. This is further challenging in scenarios where the data at a site cannot be shared with a global or central site for retraining. In this case, the model should be able to adapt and learn online with data only from the site where it is deployed. 
     Standard deep learning models are trained on centralized training data. Performance of a deep learning models may be adversely affected from site-specific variabilities like machine make, software versions, patient demographics, and site-specific clinical preferences. Federated learning enables incremental site-specific tuning of the global model to create local versions. Such models are more robust to site specific variabilities. Local models from multiple local sites are then further sent to the cloud using encrypted communication for fine tuning of the global model. During the process performance standard has to be maintained in global and local test dataset to adhere to regulatory authorities. 
     The present disclosure provides for a data generation framework that enables estimating and generating synthetic or generative samples derived from global and local datasets to resolve the issue of data sharing privacy. Tuning/retraining of the weights and global model updating occurs utilizing the synthetic or generative samples (mitigating the issue of data privacy) from a distribution that closely resembles global and local dataset distribution in a federated-like learning framework. This enables local learning at the site level to account for site-specific preferences while maintaining global performance (mitigating the issue of catastrophic forgetting). 
     Distributed local incremental learning and fine tuning ensures better performance compared to a global model trained with data from foreign sites. Such a model by design is generalizable across multiple industries including aviation, healthcare, power, additive manufacturing, and robotics. By making the updating of the weights of the global model dependent on the synthetic or generative samples derived from the global dataset, it ensures the licensed/validated global model architecture is maintained and local weights are fine tuned to better fit to local preferences, thus, improving performance over time without catastrophic forgetting. 
     For example, as described below, a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites may be established. The retraining or retuning of the global model and the respective local models occurs without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets. This enables the diversity of the data distribution to be captured at the particular sites (e.g., local sites). Also, the efficiency of training (i.e., retraining/retuning) is increased. 
     As mentioned above, the presence of datasets that significantly differ from the training dataset in terms of appearance/contrast, shape (of organs/structures of interest) and field of view, model performance may be adversely affected. For example, differences in T2-weighted contrast between MRI images of pediatric patients and adult patient cause deep learning segmentation failures. The present disclosure provides a hybrid framework for unsupervised learning and supervised learning of transforming contrast for T2-weighted images. The disclosed embodiments enable local sites and a global site to utilize a tissue specific regression model (e.g., non-linear regression model) to transform the contrast of a T2-weighted pediatric image to match the contrast of a T2-weighted adult image (or vice versa). It should be noted although the following examples are discussed with regard to variable contrast differences between a cohort of pediatric images and a cohort of adult images, the disclosed techniques may apply to any two cohorts having variable contrast differences. The disclosed embodiments enable transforming contrast similar to a training dataset improves deep learning performance for vertebra segmentation (or the segmentation of any organ or structure of interest). The disclosed techniques even though discussed with regard to the vertebrae can apply to other organs or structures (e.g., knee, brain, etc.) as long as different tissue types can be derived from the images. Such guided non-linear contrast transformation enables current deep learning models (e.g., at local sites and/or a global site) to perform without failures. Compared to a deep learning GAN-based model for contrast transformation/generation that often requires large paired or un-paired dataset for learning the transformation parameters, the hybrid model learns from a single dataset without any manual intervention or annotation and graphics processing unit requirement resulting in minimal disruption in local sites. In addition, no retraining of the existing deep learning model (e.g., at the local site) is necessary for performance improvement of the existing model. 
     With the preceding in mind, and by way of providing useful context,  FIG.  1    depicts a schematic diagram of a continuous federated learning scheme or scenario  10 . Standard deep learning models are trained on centralized training data. Performance of a deep learning models may be adversely affected from site-specific variabilities like machine make, software versions, patient demographics and site-specific clinical preferences. As depicted, the continuous federated learning scheme  10  includes a global site  12  (e.g., central or main site) and multiple local sites or nodes  14  (e.g., remote from the global site  12 ). The global site  12  includes a global model  16  (e.g., global neural network or machine learning model) trained on a primary dataset  17  (e.g., global dataset). Federated learning enables incremental site-specific tuning of the global model  16  (via local incremental learning on local data) to create local versions  18 . Such models are more robust to site specific variabilities. Local models  18  (e.g., local neural network or machine learning model) from local sites  14  are then further sent to the cloud using encrypted communication for fine tuning of the global model  16 . During the process, a performance standard has to be maintained in global and local test datasets. 
     In the continuous federated learning scenario  10 , the global model  16  is deployed across multiple sites  14  that cannot export data. A site-specific ground truth is generated using auto-curation models that may use segmentation, registration machine learning, and/or deep learning models. The site-specific ground truth may have to be refined depending on local preferences of the expert. An automatically generated and refined ground truth is then further used for local training of the models. Selective local updates of the weights of the global model  16  creates a local mutant  18  of the global model  16 . The weights of the local models  18  are then encrypted and sent to the central server for selective updating of the global model  16  as indicated by block  20 . These local updates or site-specific preferences (e.g., weights) from the local sites  14  are combined when updating the global model  16  at the global site  12 . The global model update would be strategic and would be dependent on domain and industry specific requirements. 
       FIG.  2    depicts a continuous federated learning scheme or scenario  22  that utilizes distributed data generative models to resolve issues with data privacy (or high volume data that cannot be stored) and catastrophic forgetting. The continuous federated learning scheme  22  includes a global site  12  (e.g., central or main site) and multiple local sites or nodes  14  (e.g., remote from the global site  12 ). The global site  12  includes a global model  16  (e.g., global neural network or machine learning model) trained on a primary dataset  17  (e.g., global dataset) of actual or true data. During multi-site deployment, each local site  14  also receives the global model  16  as initially trained at the global site  12 . At the global site  12 , a generative model  24  (e.g., global generative model) utilizes the primary dataset  17  to synthesize or generate a synthetic or generated (e.g., generative) dataset  26  (e.g., global synthetic or generated dataset) similar to the primary dataset  17 . The synthesized or generated dataset  26  derived from the primary dataset  17  reflects the distribution of the actual or true data in the primary dataset  17 . The generative model  24  may be created utilizing variational autoencoders, a generative adversarial network, data augmentation, and/or regression methods. The generative model  24  and the generated dataset  26  are distributed to each of the local sites  14  via multi-site deployment. 
     At the local sites  14 , the generated dataset  26  and a local dataset (actual or true local data) are combined for utilization in the local retuning/retraining of the global model  16  to generate a new local model  18 . Also, at the local sites  14 , a local generative model  28  is created from the generative model  24  and the local dataset. The local generative model  28  utilizes the local dataset to synthesize or generate a synthetic or generated (e.g., generative) dataset  30  (e.g., local synthetic or generated dataset) similar to the primary dataset  17 . The local synthesized or generated dataset  30  derived from the local dataset reflects the distribution of the actual or true data in the local dataset  17 . The new local models  18 , the local generative models  28 , and the local generated datasets  30  from each of the local sites  14  are then encrypted and sent to the central server for selective updating/retuning/retraining of the global model  16  as indicated by block  32 . A retrained global model may then be provided to the local sites  14 . This process may occur in an iterative manner. Over time, after repeating the cycle iteratively, the respective local generative model  28  and the generative model  24  should eventually have the same distribution (i.e., the models  24 ,  28  will converge at least with regard to mean and variance). 
     Retraining using synthetic samples similar to global and local datasets ensures data privacy and mitigates catastrophic forgetting. Creating generative models configured to generate synthetic samples similar to those at the global and local sites ensures global and local data distribution is captured enabling training (e.g., retraining) of a neural network in a continuous federated learning framework without data sharing. 
     The global site  12  and the local sites  14  may be arranged in a centralized arrangement as depicted in  FIG.  3   . For example, the global site  12  may be located at one or more central or main servers or computing devices at a central or main site. The local sites or nodes  14  may be located remotely from the location of the global site  12 . Each local site or node  14  may include one or more servers or computing devices. The global site  12  and the local sites  14  may be interconnected via the Internet. In certain embodiments, the global site  12  and the local sites may be interconnected via a cloud or a cloud computing environment. As used herein, the term “cloud” or “cloud computing environment” may refer to various evolving arrangements, infrastructure, networks, and the like that will typically be based upon the Internet. The term may refer to any type of cloud, including client clouds, application clouds, platform clouds, infrastructure clouds, server clouds, and so forth. 
     Alternatively, the global site  12  and the local sites  14  may be arranged in a decentralized arrangement as depicted in  FIG.  4   . In the decentralized arrangement, the global site  12  does not truly exist (but is maintained, for example, in a cloud environment) by the local sites  14  which are configured to coordinate between themselves. For example, a cloud computing environment  36  includes a plurality of distributed nodes  14  (e.g., local sites). The computing resources of the nodes  14  are pooled to serve multiple consumers, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. Examples of resources include storage, processing, memory, network bandwidth, and virtual machines. The nodes  14  may communicate with one another to distribute resources, and such communication and management of distribution of resources may be controlled by a cloud management module, residing on one or more nodes  14 . The nodes  14  may communicate via any suitable arrangement and protocol. Further, the nodes  14  may include servers associated with one or more providers. For example, certain programs or software platforms may be accessed via a set of nodes  14  provided by the owner of the programs while other nodes  14  are provided by data storage companies. Certain nodes  14  may also be overflow nodes that are used during higher load times. 
       FIG.  5    is a block diagram of a processor-based device or system  38  that may be configured to implement functionality described herein in accordance with one embodiment. Various functionality, as described herein, may be performed by, or in conjunction with, a processor-based system  38 , which is generally depicted in  FIG.  5    in accordance with one embodiment. For example, the various computing devices or servers (e.g., utilized at the global site and/or local sites) herein may include, or be partially or entirely embodied in, a processor-based system, such as that presently illustrated. The processor-based system  38  may be a general-purpose computer, such as a personal computer, configured to run a variety of software, including software implementing all or part of the functionality described herein. Alternatively, in other embodiments, the processor-based system  38  may include, among other things, a distributed computing system, or an application-specific computer or workstation configured to implement all or part of the presently described functionality based on specialized software and/or hardware provided as part of the system. Further, the processor-based system  38  may include either a single processor or a plurality of processors to facilitate implementation of the presently disclosed functionality. 
     In one embodiment, the exemplary processor-based system  38  includes a microcontroller or microprocessor  40 , such as a central processing unit (CPU), which executes various routines and processing functions of the system  38 . For example, the microprocessor  40  may execute various operating system instructions, as well as software routines configured to effect certain processes, stored in or provided by a manufacture including one or more computer readable-media (at least collectively storing the software routines), such as a memory  42  (e.g., a random access memory (RAM) of a personal computer) or one or more mass storage devices  44  (e.g., an internal or external hard drive, a solid-state storage device, a CD-ROM, a DVD, or another storage device). In addition, the microprocessor  40  processes data provided as inputs for various routines or software programs, such as data provided as part of the present subject matter described herein in computer-based implementations. 
     Such data may be stored in, or provided by, the memory  42  or mass storage device  44 . The memory  42  or the mass storage device may store various datasets (e.g., actual datasets such as the global dataset or local dataset, local synthetic dataset, global synthetic dataset, etc.), various deep learning or machine learning models (e.g., global modes, local models, global generative model, local generative model, etc.), and other information. Alternatively, such data may be provided to the microprocessor  40  via one or more input devices  46 . The input devices  46  may include manual input devices, such as a keyboard, a mouse, touchscreen (e.g., on tablet), or the like. In addition, the input devices  46  may include a network device, such as a wired or wireless Ethernet card, a wireless network adapter, or any of various ports or devices configured to facilitate communication with other devices via any suitable communications network, such as a local area network or the Internet. Through such a network device, the system  38  may exchange data and communicate with other networked electronic systems, whether proximate to or remote from the system  38 . 
     Results generated by the microprocessor  40 , such as the results obtained by processing data in accordance with one or more stored routines, may be provided to an operator via one or more output devices  48  (e.g., a display). Communication between the various components of the processor-based system  38  may typically be accomplished via a chipset and one or more busses or interconnects which electrically connect the components of the system  38 . 
       FIG.  6    is a flow chart of a method  50  for retraining local and global models in a continuous federated learning framework. One or more components (e.g., processor-based devices  38  in  FIG.  5   ) of the global site  12  and/or the local sites  14  in  FIG.  2    may be utilized for performing the method  50 . One or more steps of the method  50  may be performed simultaneously or in a different order from that depicted in  FIG.  6   . It is assumed in the continuous federated learning framework that global and local datasets are available at the global site and the local sites, respectively, and generated trained models. 
     The method  50  includes establishing a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites (block  52 ). Establishing the continuous federated learning framework may include generating a trained global model (e.g., utilizing an actual global dataset) and validating the trained global model (e.g., utilizing an actual global test dataset held out from or separate from the actual global dataset) at a global site (e.g., central or main site). Establishing the continuous federated learning framework may also include providing the trained global model to multiple local sites or nodes remote from the global site. This may include at each local site accessing the trained global model from a database or memory available to each local site. 
     The method  50  also includes retraining or retuning the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic or generative datasets generated from the actual datasets (block  54 ). Actual datasets (e.g., actual global dataset and respective actual local datasets) are not shared (e.g., due to data privacy or a high volume of data that cannot be stored) between the local sites or between the global site and the local sites. 
       FIG.  7    is a flow chart of a method  56  for retraining local and global models in a continuous federated learning framework. One or more components (e.g., processor-based devices  38  in  FIG.  5   ) of the global site  12  and/or the local sites  14  in  FIG.  2    may be utilized for performing the method  50 . One or more steps of the method  56  may be performed simultaneously or in a different order from that depicted in  FIG.  7   . It is assumed in the continuous federated learning framework that global and local datasets are available at the global site and the local sites, respectively, and generated trained models. It also assumed that each local site includes a trained local model (e.g., trained on an actual local dataset) derived from the trained global model at the global site. 
     The method  56  includes, at the global site, creating or generating a generative model configured to generate a synthetic or generated global dataset similar to and based on the actual global dataset utilizing the actual global dataset (block  58 ). In certain embodiments, the generative model may be created utilizing variational autoencoders, a generative adversarial network, data augmentation, and/or regression methods. In certain embodiments, the generative model (e.g., global generative model) is configured to perform guided non-linear guided transformation between pediatric and adult vertebrae magnetic resonance (MR) images. In certain embodiments, the generative model is configured to transform a contrast of pediatric vertebrae MR images to resemble a contrast of adult vertebrae MR images. In certain embodiments, the generative model is configured to transform a contrast of pediatric vertebrae MR images to resemble a contrast of adult vertebrae MR images. In certain embodiments, the generative model is a tissue specific non-linear regression model. In certain embodiments, the generative model is configured, prior to performing guided non-linear contrast transformation, to utilize expectation maximization clustering to identify different tissue classes in vertebrae MR images. 
     The method  56  also includes, at the global site, providing the generative model and the synthetic global dataset to each of the respective local sites (block  60 ). The method  56  further includes, at each local site, retraining or retuning each respective local model utilizing both the synthetic global dataset and an actual local dataset at the respective local site to locally retune weights to generate a new respective local model (block  62 ). The method  56  even further includes, at each local site, validating each new respective local model utilizing an actual local test dataset at the respective local site (without catastrophic forgetting) (block  64 ). The actual local test dataset is held out from or separate from the actual local dataset utilized for training the local model and generating the generative local dataset. The method  56  still further includes, at each local site, creating or generating a local generative model configured to generate a synthetic or generated local dataset similar to and based on the actual local dataset utilizing the actual local dataset (block  66 ). The global generative model may also be utilized in generating the local generative model. In particular, the global generative model at the local site may be retuned or retrained utilizing the actual local dataset. In certain embodiments, the local generative model is configured to perform guided non-linear contrast transformation between pediatric and adult vertebrae magnetic resonance (MR) images. In certain embodiments, the local generative model is configured to transform a contrast of pediatric vertebrae MR images to resemble a contrast of adult vertebrae MR images. In certain embodiments, the local generative model is configured to transform a contrast of adult vertebrae MR images to resemble a contrast of adult vertebrae MR images. In certain embodiments, the local generative model is a tissue specific non-linear regression model. In certain embodiments, the local generative model is configured, prior to performing guided non-linear contrast transformation, to utilize expectation maximization clustering to identify different tissue classes in vertebrae MR images. In certain embodiments, the local generative model may be trained utilizing an actual local dataset. In particular, the local generative model may be trained utilizing a couple of un-paired T2-weighted MR images comprising a T2-weighted pediatric vertebrae MR image and a T2-weighted adult vertebrae MR image. 
     The method  56  includes, at each local site, providing the respective local generative model, the new respective local model, and the respective synthetic local dataset to the global site (block  68 ). The method  56  also includes, at the global site, validating each new respective local model utilizing an actual global test dataset (block  70 ). The actual global test dataset is held out from or separate from the actual global dataset utilized for training the global model and generating the generative global dataset. 
     The method  56  includes, at the global site, retraining or retuning the global model utilizing the respective synthetic local datasets from each of the respective local sites to retune global weights to generate a retrained global model (block  72 ). The method  56  also includes, at the global site, validating the retrained global model utilizing the actual global test dataset (block  74 ). The method  56  further includes, at the global site, providing the retrained global model to each of the respective local sites (block  76 ). The steps of the method  56  may then be repeated in an iterative manner. 
     The systems and methods described above may be utilized on a variety of types of data in various industries (e.g., healthcare, aviation, etc.). One example of data that may be utilized is imaging data (e.g., medical imaging data) acquired from medical imaging systems.  FIGS.  8  and  9    provide examples of synthetic medical images generated by global or local generative models via simulated data distribution based on actual medical images.  FIG.  8    includes synthetic images  78 ,  80 , and  82  of magnetic resonance (MR) images of the brain acquired with a fluid-attenuated inversion recovery (FLAIR) MRI sequence. Circles  84  indicate synthetic lesions generated in the synthetic images  78 ,  80 , and  82 .  FIG.  9    includes synthetic images  86 ,  88 , and  90  of MR images of the brain acquired with a T2 MRI sequence. Circles  92  indicate synthetic lesions generated in the synthetic images  86 ,  88 , and  90 . 
       FIG.  10    is an embodiment of a schematic diagram of utilization of a trained generative model  94  (e.g., generative model  24  or local generative model  28  in  FIG.  2   ) for guided tissue contrast transformation between pediatric vertebrae MR images and adult vertebrae MR images. As depicted, an image  96  (e.g., test image) is inputted into the trained generative model  94 . In certain embodiments, the image  96  is derived from actual data at the global site or the local site. As depicted, the image  96  is a pediatric vertebrae MR image (e.g., T2-weighted pediatric vertebrae MR image). In certain embodiments, the image  96  is an adult vertebrae MR image (e.g., T2-weighted adult vertebrae MR image). The trained generative model  94  automatically performs tissue specific decomposition (e.g.., utilizing expectation maximization (EM)-based clustering) as indicated by reference numeral  98  to identify different classes of tissue within the image  96 . In particular, a bone/air decomposition image  100 , a muscle/soft bone image  102 , a muscle/spinal cord image  104 , and a fat decomposition image  106  are generated from the image  96 . The trained generative model  94  than utilizes non-linear regression to generate a predicted tissue contrast image  108  from the decomposition images  100 ,  102 ,  104 , and  106 . As depicted, the predicted tissue contrast image  108  is a pediatric vertebrae MR image (e.g., T2-weighted pediatric vertebrae MR image) having a contrast/intensity that matches a contrast/intensity of an adult vertebrae MR image (e.g., T2-weighted adult vertebrae MR image). In certain embodiments (when the inputted image is an adult vertebrae MR image), the predicted tissue contrast image  108  is an adult vertebrae MR image (e.g., T2-weighted adult vertebrae MR image) having a contrast/intensity that matches a contrast/intensity of a pediatric vertebrae MR image (e.g., T2-weighted pediatric vertebrae MR image). Thus, the trained generative model  94  performs guided non-linear contrast transformation between pediatric and adult vertebrae MR images. As depicted, the trained generative model  94  utilizes tissue specific regression models (e.g., bone/air regression model  110 , muscle/soft bond regression model  112 , and spinal cord/fat regression model  114 ) for the guided non-linear contrast transformation. The predicted tissue contrast image  108  is better for the performance of segmentation utilizing a deep learning-based segmentation model (e.g., global model  16  or local model  18 ). As noted above, a trained generative model may also be utilized for guided tissue contrast transformation between other pairs of cohorts (besides pediatric and adult) as long as there are variable contrast differences between the pair of cohorts. Also, the trained generative model may be utilized for other organs or anatomical structures (besides vertebrae) as long as different tissue types can be derived from the images. 
       FIG.  11    is an embodiment of a flow chart of a method  116  for utilizing a trained generative model for guided tissue contrast transformation between pediatric vertebrae magnetic resonance images and adult vertebrae magnetic resonance images. One or more components (e.g., processor-based devices  38  in  FIG.  5   ) of the global site  12  and/or the local sites  14  in  FIG.  2    may be utilized for performing the method  116 . One or more steps of the method  116  may be performed simultaneously or in a different order from that depicted in  FIG.  11   . It is assumed in the continuous federated learning framework that global and local datasets are available at the global site and the local sites, respectively, and generated trained models. It also assumed that each local site includes a trained local model (e.g., trained on an actual local dataset) derived from the trained global model at the global site. 
     The method  116  includes inputting an image (e.g., test image) is inputted into a trained generative model (block  118 ). In certain embodiments, the image is derived from actual data at the global site or the local site. In certain embodiments, the inputted image is a pediatric vertebrae MR image (e.g., T2-weighted pediatric vertebrae MR image). In certain embodiments, the inputted image is an adult vertebrae MR image (e.g., T2-weighted adult vertebrae MR image). The method  116  also includes utilizing the trained generative model to automatically perform tissue specific decomposition (e.g.., utilizing expectation maximization (EM)-based clustering) to identify different classes of tissue within the image (block  120 ). In particular, a bone/air decomposition image, a muscle/soft bone image, a muscle/spinal cord image, and a fat decomposition image are generated from the inputted image. The method  116  further includes, via the trained generative model, utilizing non-linear regression to generate a predicted tissue contrast image from the decomposition images (block  122 ). In certain embodiments (e.g., when the inputted image is a pediatric vertebrae MR image), the predicted tissue contrast image is a pediatric vertebrae MR image (e.g., T2-weighted pediatric vertebrae MR image) having a contrast/intensity that matches a contrast/intensity of an adult vertebrae MR image (e.g., T2-weighted adult vertebrae MR image). In certain embodiments (when the inputted image is an adult vertebrae MR image), the predicted tissue contrast image is an adult vertebrae MR image (e.g., T2-weighted adult vertebrae MR image) having a contrast/intensity that matches a contrast/intensity of a pediatric vertebrae MR image (e.g., T2-weighted pediatric vertebrae MR image). Thus, the trained generative model performs guided non-linear contrast transformation between pediatric and adult vertebrae MR images. In particular, the trained generative model utilizes tissue specific regression models (e.g., bone/air regression model, muscle/soft bond regression model, and spinal cord/fat regression model) for the guided non-linear contrast transformation. The predicted tissue contrast image is better for the performance of segmentation utilizing a deep learning-based segmentation model (e.g., global model  16  or local model  18 ). A similar method may also be utilized for guided tissue contrast transformation between other pairs of cohorts (besides pediatric and adult) as long as there are variable contrast differences between the pair of cohorts. Also, a similar method may be utilized for other organs or anatomical structures (besides vertebrae) as long as different tissue types can be derived from the images. 
       FIG.  12    is an embodiment of a flow chart of a method  124  for training a generative model for guided tissue contrast transformation between pediatric vertebrae magnetic resonance images and adult vertebrae magnetic resonance images. One or more components (e.g., processor-based devices  38  in  FIG.  5   ) of the global site  12  and/or the local sites  14  in  FIG.  2    may be utilized for performing the method  124 . One or more steps of the method  124  may be performed simultaneously or in a different order from that depicted in  FIG.  12   . It is assumed in the continuous federated learning framework that global and local datasets are available at the global site and the local sites, respectively, and generated trained models. 
     The method  124  includes inputting into an untrained neural network two clinical un-paired T2-weighted MR images as the training dataset (block  126 ). The un-paired T2-weighted MR images include a T2-weighted pediatric vertebrae MR image and a T2-weighted adult vertebrae MR image. One of the images comes from the training dataset (e.g., at the local site) and the others from failure cases. In certain embodiments, the T2-weighted adult vertebrae MR image is from the training dataset and the T2-weighted pediatric vertebrae MR image is from the failure cases. In certain embodiments, the T2-weighted pediatric vertebrae MR image is from the training dataset and the T2-weighted adult vertebrae MR image is from the failure cases. The training dataset comes from the actual dataset at the local site. The method  124  includes performing unsupervised EM-based clustering on the two inputted un-paired T2-weighted MR images to identify different tissue classes (block  128 ). The different tissue classes include dense bone/air decomposition, a muscle/soft bone, muscle/spinal cord (vertebrae), and fat. The method  124  further includes generating from the two inputted un-paired T2-weighted MR images a non-linear random regression model (e.g., the trained generative model) that takes probabilities of individual classes (e.g., the tissue classes) from the failure case to predict T2-weighted intensity of the training case (block  130 ). The non-linear random regression model includes three tissue specific regression models (e.g., bone/air regression model, muscle/soft bond regression model, and spinal cord/fat regression model) for the guided non-linear contrast transformation of T2-weighted intensities/contrast to match contrast similar to a training dataset. The method  124  even further includes validating the trained generative model utilizing a new patient MRI image (e.g., vertebrae image) (e.g., acquired at the local site) (block  132 ). The EM-based clustering of the trained generative model identifies the tissues classes (e.g., via automatic tissue specific decomposition). The tissue specific regression models of the trained generative model are utilized to transform T2-weighted MR intensities of the new patient MRI image (e.g., test case) to be similar to (or match) the training values. 
       FIG.  13    illustrates examples of predictions from segmentation on original T2-weighted MR images and transformed T2-weighted MR images. Images  134  and  136  are predicted T2-weighted MR images derived from segmentation utilizing a deep learning-based model for segmentation of vertebrae in original (i.e., not subject to contrast transformation utilizing the trained generative model  94  in  FIG.  10   ) T2-weighted pediatric vertebrae images. Corresponding images  138  and  140  are predicted T2-weighted MR images derived from segmentation utilizing the deep-learning based model for segmentation of vertebrae in transformed (i.e., generated from contrast transformation of the corresponding original T2-weighted MR images with the trained generative model  94  in  FIG.  10   ) T2-weighted adult vertebrae images. The deep-learning based model utilized 3D UNET segmentation. The segmentation of the vertebrae (as indicated by labels  142 ) is improved in images  138  and  140  derived from transformed MR images. 
       FIG.  14    illustrates an example of an un-paired dataset utilized for training a generative model for guided tissue contrast transformation between pediatric vertebrae magnetic resonance images and adult vertebrae magnetic resonance images. Image  144  is a T2-weighted pediatric vertebrae MR image. Image  146  is a T2-weighted adult vertebrae MR image. Images  144  and  146  are utilized to train a generative model (e.g., generative model  94  in  FIG.  10   ). 
       FIG.  15    illustrates the application of tissue contrast transformation to a pediatric vertebrae MR image. Image  148  is a T2-weighted pediatric vertebrae MR image. Image  150  is a transformed T2-weighted pediatric vertebrae MR image derived from the image  148  utilizing a trained generative model (e.g., generative model  94  in  FIG.  10   ). 
       FIG.  16    illustrates different tissue decomposition images derived from adult and pediatric T2-weighted images for regression training. Row  152  includes a T2-weighted adult vertebrae MR image  154 . Row  152  also includes a bone/air decomposition image  156 , a muscle/soft bone image  158 , a muscle/spinal cord image  160 , and a fat decomposition image  162  derived from image  154  (e.g., via EM-based clustering). Row  164  includes a T2-weighted adult vertebrae MR image  166 . Row  164  also includes a bone/air decomposition image  168 , a muscle/soft bone image  170 , a muscle/spinal cord image  172 , and a fat decomposition image  174  derived from image  166  (e.g., via EM-based clustering) 
     Technical effects of the disclosed subject matter include providing systems and methods for utilizing a continuous federated learning framework that utilizes training of local and global models with generated data similar to the actual local and global datasets mitigating the issue of catastrophic forgetting and data sharing concerns. The approach is operator independent and also provides local adaptation and site-specific customization. In addition, the approach reduces time related to retraining/testing, especially in areas (e.g., healthcare) where it is difficult to share training data. Further, the approach provides online learning of site-specific data. 
     The techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function]...” or “step for [perform]ing [a function]...”, it is intended that such elements are to be interpreted under 35 U.S.C. 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. 112(f). 
     This written description uses examples to disclose the present subject matter, including the best mode, and also to enable any person skilled in the art to practice the subject matter, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.