Patent Publication Number: US-11651584-B2

Title: System and method for memory augmented domain adaptation

Description:
BACKGROUND 
     Embodiments of the present specification relate generally to machine learning, and more particularly to systems and methods for memory augmented continuous learning to adapt to a domain. 
     As will be appreciated, traditional machine learning techniques require a large dataset to “learn” via extensive training. Also, the machine learning techniques are typically trained using a dataset corresponding to a source domain. However, most statistical methods including machine learning techniques are known to perform rather poorly in rarely encountered scenarios/domains. In particular, many machine learning techniques including neural networks encounter the problem of domain adaptation and hence suffer from degradation of performance of a model on samples associated with a different but related domain. Problems in domain adaptation are typically attributed to diversity in the samples. This diversity in the samples is encountered even in controlled environments like medical imaging where training samples differ due to differences in equipment, demography, pathological conditions, protocol/operator variability, patients/subjects, and the like. Moreover, the problem with domain adaptation is further compounded by difficulty in obtaining voluminous data in healthcare and other regulated domains for training or retraining the models. 
     It is desirable that an algorithm/technique trained using data corresponding to a source domain adapts to a new target domain using as few samples as possible. Certain currently available solutions to the problem of domain adaptation entail enabling systems to learn from errors during deployment. One example approach calls for correcting the observed errors by retraining the existing technique with new samples. However, such approaches disadvantageously suffer from drawbacks. In one example, adapting the algorithm to the new target domain requires a large number of samples corresponding to the target domain. Also, in another example, neural networks suffer from a phenomenon known as “catastrophic forgetting.” 
     Moreover, some presently available techniques use Memory Augmented Neural Networks (MANN) for remembering rare events. Certain other techniques use a few-shot learning method for adapting quickly to changes in either the domain or task through the meta-learning paradigm. However, most of these approaches disadvantageously rely on meta-learning from many classes. 
     BRIEF DESCRIPTION 
     In accordance with aspects of the present specification, a system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform, where the memory augmented domain adaptation platform is configured to compute one or more features corresponding to an input image, where the input image corresponds to a target domain, identify a set of support images based on the one or more features corresponding to the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both the set of features and the set of masks corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both the set of features and the set of masks corresponding to the set of support images. Additionally, the system includes an interface unit configured to present the output for analysis. 
     In accordance with another aspect of the present specification, a processing system for adapting a machine-learnt model is presented. The processing system includes a memory augmented domain adaptation platform configured to compute one or more features corresponding to an input image, where the input image corresponds to a target domain, identify a set of support images based on the one or more features corresponding to the input image, where the set of support images corresponds to the target domain, augment an input to the machine-learnt model with a set of features, a set of masks, or both the set of features and the set of masks corresponding to the set of support images to adapt the machine-learnt model to the target domain, generate an output based at least on the set of features, the set of masks, or both the set of features and the set of masks corresponding to the set of support images, and provide the output to facilitate analysis. 
     In accordance with yet another aspect of the present specification, a method for adapting a machine-learnt model is presented. The method includes receiving an input image, where the input image corresponds to a target domain. Further, the method includes computing one or more features corresponding to the input image. Moreover, the method includes identifying a set of support images based on the one or more features corresponding to the input image, where the set of support images corresponds to the target domain. In addition, the method includes augmenting an input to the machine-learnt model with a set of features, a set of masks, or both the set of features and the set of masks corresponding to the set of support images to adapt the machine-learnt model to the target domain The method also includes generating an output based at least on the set of features, the set of masks, or both the set of features and the set of masks corresponding to the set of support images. Furthermore, the method includes outputting the output to facilitate analysis. 
    
    
     
       DRAWINGS 
       These and other features, aspects, and advantages of the present disclosure 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 a schematic representation of an exemplary system for memory augmented domain adaptation, in accordance with aspects of the present specification; 
         FIG.  2    is a flow chart of an exemplary method for memory augmented domain adaptation, in accordance with aspects of the present specification; 
         FIG.  3    is a schematic representation illustrating an exemplary method for memory augmented domain adaptation, in accordance with aspects of the present specification; 
         FIGS.  4 ( a )- 4 ( c )  are diagrammatical representations of different datasets corresponding to different domains for use in the system and method for memory augmented domain adaptation, in accordance with aspects of the present specification; and 
         FIGS.  5 ( a )- 5 ( e )  are diagrammatical representations of a comparison of performance of different methods domain adaptation, in accordance with aspects of the present specification. 
     
    
    
     DETAILED DESCRIPTION 
     The following description presents exemplary systems and methods for memory augmented domain adaptation. Particularly, embodiments described hereinafter present exemplary systems and methods that facilitate enhanced memory augmented continuous learning for adapting a machine-learnt model to a new domain to deliver better performance with a relatively small set of samples. For example, the systems and methods facilitate enhanced performance of tasks such as classification and segmentation when the machine-learnt model is deployed in a target domain using a small set of samples. Moreover, the systems and methods presented hereinafter provide an elegant solution to circumvent drawbacks associated with currently available methods. In particular, the systems and methods for memory augmented domain adaptation present a learning technique to adapt a machine-learnt model to newer domains with as few samples as possible. The newer samples associated with the deployed target domain are “remembered” so that the output generated by the present systems and methods is constantly evolving while circumventing any modifications to the base. 
     The systems and methods entail use of a “meta-learning” technique designed to enable improvements in the performance of the machine-learnt model when deployed in new domains In particular, the system includes a memory unit which acts like a programmable memory and is used to continuously learn and facilitate adaptation to a target domain using a small set of samples corresponding to the target domain Accordingly, when a similar case is subsequently encountered by the machine-learnt model, the memory unit is queried for a match. More particularly, the additional memory unit facilitates retrieval of samples similar to a particular use-case and the corresponding annotations. Furthermore, the annotations corresponding to the retrieved samples stored in the memory unit may be revised, thereby providing enhanced control over the subsequent predictions by the machine-learnt model. It may be noted that the terms domain and site may be used interchangeably. 
     For clarity, exemplary embodiments of the present systems and methods are described in the context of a medical imaging system. It may be noted that although the exemplary embodiments illustrated hereinafter are described in the context of a medical imaging system, other imaging systems and applications such as industrial imaging systems and non-destructive evaluation and inspection systems, such as pipeline inspection systems, liquid reactor inspection systems, are also contemplated. Some examples of the medical imaging system may include a computed tomography (CT) system, a single photon emission computed tomography system (SPECT) system, an X-ray imaging system, a magnetic resonance imaging (MRI) system, an optical imaging system, and/or an ultrasound imaging system. Additionally, the exemplary embodiments illustrated and described hereinafter may find application in multi-modality imaging systems that employ an X-ray imaging system in conjunction with other imaging modalities, position-tracking systems or other sensor systems. In one example, the multi-modality imaging system may include a positron emission tomography (PET) imaging system-X-ray imaging system. Furthermore, in other non-limiting examples of the multi-modality imaging systems, the X-ray imaging system may be used in conjunction with other imaging systems, such as, but not limited to, a computed tomography (CT) imaging system, a contrast enhanced ultrasound imaging system, an ultrasound imaging system, an optical imaging system, a magnetic resonance (MR) imaging system and other imaging systems, in accordance with aspects of the present specification. An exemplary environment that is suitable for practicing various implementations of the present system and methods is discussed in the following sections with reference to  FIG.  1   . 
       FIG.  1    illustrates an exemplary imaging system  100  configured to receive and process an input image corresponding to a target domain corresponding to a target volume in a subject  102  such as a patient or a non-biological object to generate an output, where the output is used for further analysis. In particular, the system  100  is configured to use an exemplary memory augmented domain adaptation technique to adapt a machine-learnt model  106  to a target domain It may be noted that the machine-learnt model  106  is typically trained using a dataset corresponding to a source domain. The system  100  is configured to adapt the machine-learnt model  106  to the target domain using a relatively small set of samples corresponding to the target domain. In one embodiment, the imaging system  100  for example, may include an X-ray imaging system, a PET system, a SPECT system, a CT imaging system, an MRI system, a hybrid imaging system, and/or a multi-modality imaging system. 
     In one embodiment, the patient  102  may be suitably positioned, for example, on a table to allow the system  100  to image the target volume of the patient  102 . During imaging, an image acquisition device  104  that is operatively coupled to a medical imaging system  108  may be used to acquire image data corresponding to an object or the target volume/region of interest in the patient  102 . However, in certain other embodiments, the input image may be retrieved from a data storage. 
     Additionally, the medical imaging system  108  is configured to receive an input image or image data corresponding to the patient  102  and process the image data to generate an output corresponding to the patient  102 . In a presently contemplated configuration, the system  100  may be configured to acquire image data representative of the patient  102 . As noted hereinabove, in one embodiment, the system  100  may acquire image data corresponding to the patient  102  via the image acquisition device  104 . Also, in one embodiment, the image acquisition device  104  may include a probe, where the probe may include an invasive probe, or a non-invasive or external probe, such as an external ultrasound probe, that is configured to aid in the acquisition of image data. Also, in certain other embodiments, image data may be acquired via one or more sensors (not shown) that may be disposed on the patient  102  or via use of other means of acquiring image data corresponding to the patient  102 . By way of example, the sensors may include physiological sensors (not shown) such as positional sensors. In certain embodiments, the positional sensors may include electromagnetic field sensors or inertial sensors. These sensors may be operationally coupled to a data acquisition device, such as an imaging system, via leads (not shown), for example. Other methods of acquiring image data corresponding to the patient  102  are also contemplated. 
     Moreover, the medical imaging system  108  may include an acquisition subsystem  110  and a processing subsystem  112 , in one embodiment. Further, the acquisition subsystem  110  of the medical imaging system  108  is configured to acquire image data or an input image representative of the patient  102  via the image acquisition device  104 , in one embodiment. It may be noted that the terms image, image frames, and input image may be used interchangeably. 
     In addition, the acquisition subsystem  110  may also be configured to acquire images stored in an optical data storage article (not shown). It may be noted that the optical data storage article may be an optical storage medium, such as a compact disc (CD), a digital versatile disc (DVD), multi-layer structures, such as DVD-5 or DVD-9, multi-sided structures, such as DVD-10 or DVD-18, a high definition digital versatile disc (HD-DVD), a Blu-ray disc, a near field optical storage disc, a holographic storage medium, or another like volumetric optical storage medium, such as, for example, two-photon or multi-photon absorption storage format. Further, the 2D images so acquired by the acquisition subsystem  110  may be stored locally on the medical imaging system  108  in a data repository  116 , for example. 
     Additionally, the image data acquired from the patient  102  may then be processed by the processing subsystem  112 . The processing subsystem  112 , for example, may include one or more application-specific processors, graphical processing units, digital signal processors, microcomputers, microcontrollers, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), Programmable Logic Arrays (PLAs), and/or other suitable processing devices. Alternatively, the processing subsystem  112  may be configured to store the acquired image data and/or the user input in a data repository  116  and/or in a memory unit  118  for later use. In one embodiment, the data repository  116 , for example, may include a hard disk drive, a floppy disk drive, a compact disk-read/write (CD-R/W) drive, a Digital Versatile Disc (DVD) drive, a flash drive, and/or a solid-state storage device. 
     It may be noted that the examples, demonstrations, and process steps that may be performed by certain components of the present system, for example by the processing subsystem  112 , may be implemented by suitable code on a processor-based system. To that end, the processor-based system, for example, may include a general-purpose or a special-purpose computer. It may also be noted that different implementations of the present specification may perform some or all of the steps described herein in different orders or substantially concurrently. 
     According to aspects of the present specification, the image data acquired and/or processed by the medical imaging system  108  may be employed to perform one or more tasks. In one example, the processing subsystem  112  may include the machine-learnt model  106  such as a neural network that is configured to perform the tasks. In particular, the machine-learnt model  106  may be trained using a dataset corresponding to a source domain to perform the tasks. By way of a non-limiting example, the machine-learnt model  106  may be trained to classify the input image and/or segment one or more regions in the input image to aid a clinician in providing a diagnosis. In certain embodiments, the processing subsystem  112  may be further coupled to a storage system, such as the data repository  116 , where the data repository  116  is configured to store the acquired image data. In certain embodiments, the data repository  116  may include a local database (not shown). 
     Moreover, in accordance with aspects of the present specification, the imaging system  100  may also include the memory unit  118 . Although the configuration of  FIG.  1    depicts the data repository  116  as including the memory unit  118 , in other embodiments, the memory unit  118  may be a standalone unit that is external to the data repository  116  and/or the imaging system  100 . The memory unit  118  is configured to store input images and outputs generated by the system  100 . 
     As previously noted, the presently available techniques suffer from degraded performance of a machine-learnt model when the machine-learnt model is deployed in a new target domain. In accordance with aspects of the present specification, the imaging system  100  is designed to circumvent the shortcomings of the presently available techniques. More particularly, imaging system  100  includes a memory augmented domain adaptation platform  114  that is configured to aid in the automated adaptation of the machine-learnt model to a new target domain The exemplary system  100  that includes the memory augmented domain adaptation platform  114  provides a framework for deploying the machine-learnt model  106  in the target domain by enabling the machine-learnt model  106  to adapt to the target domain using a relatively small set of images corresponding to the target domain, which will in turn simplifies the clinical workflow. In particular, the memory augmented domain adaptation platform  114  works in conjunction with the machine-learnt model  106  to enhance the adaptability of the machine-learnt model  106  to the target domain, and thereby improve the performance of the imaging system  100 . It may be noted that the terms “new domain,” “target site,” and “target domain” may be used interchangeably. 
     Also, in the presently contemplated configuration illustrated in  FIG.  1   , the processing subsystem  112  is shown as including the memory augmented domain adaptation platform  114 . However, in certain embodiments, the memory augmented domain adaptation platform  114  may also be used as a standalone unit that is physically separate from the processing subsystem  112  and the medical imaging system  108 . By way of example, the memory augmented domain adaptation platform  114  may be external to and operatively coupled to the medical imaging system  108 . 
     The exemplary memory augmented domain adaptation platform  114  is configured to circumvent the shortcomings of the presently available domain adaptation techniques. More particularly, the memory augmented domain adaptation platform  114  is configured to facilitate adaptation of the machine-learnt model to a target domain using a small set of sample images corresponding to the target domain, thereby leading to consistent outcomes. 
     As previously noted, a given model is typically trained using the dataset corresponding to the source domain. It is desirable that the machine-learnt model  106 , when deployed in the target domain adapts to the target domain, while maintaining the performance of the system  100 . Accordingly, when the imaging system  100  is deployed in the target domain, the memory augmented domain adaptation platform  114  is configured to adapt the machine-learnt model  106  to the target domain to perform a given task. By way of example, the memory augmented domain adaptation platform  114  may be configured to aid the machine-learnt model in processing the acquired input image to classify the input image and/or segment one or more regions of interest in the input image. 
     Accordingly, in operation, the machine-learnt model  106  and/or the memory augmented domain adaptation platform  114  are configured to receive an input image, where the input image corresponds to the target domain Further, the memory augmented domain adaptation platform  114  is configured to compute one or more features corresponding to the input image. The features include shape features, texture features, and the like. In certain other embodiments, other features may be used to identify the set of support images. Some non-limiting examples of the other features include age of the patient  102 , gender of the patient  102 , electronic medical record (EMR) information corresponding to the patient  102 , demography, and the like. 
     It may be noted that the memory unit  118  is configured to store one or more images corresponding to the target domain Additionally, the memory unit  118  may also be configured to store one or more features such as texture features and/or shape features and masks corresponding to the images of the target domain. Moreover, other features corresponding to the images of the target domain such as age, gender, EMR information, demography, and the like may be stored. 
     Moreover, subsequent to receipt of the input image, the memory augmented domain adaptation platform  114  is configured to query the data repository  116  and the memory unit  118  in particular to identify a matching set of images based on the features of the input image. In one example, consequent to the query, the memory augmented domain adaptation platform  114  may identify a set of support images in the memory unit  118  based on the image features corresponding to the input image. It may be noted that in accordance with aspects of the present specification, in certain embodiments, the system  100  may be configured to allow a clinician to identify the set of support images. 
     It may be noted that the set of support images corresponds to the target domain and may be a subset of images corresponding to the target domain. In one non-limiting example, the set of support images includes images in a range from about three images to about five images. However, use of other number of images in the set of support images is also contemplated. More particularly, a small subset of the target domain images may be used as the set of support images. Use of the small subset of the target domain images as the set of support images aids in circumventing the need for use of a large pool/set of training data by the currently available techniques. 
     Traditionally, the currently available techniques provide only the input image to the machine-learnt model to perform a given task. In accordance with aspects of the present specification, the memory augmented domain adaptation platform  114  is configured to augment an input to the machine-learnt model  106  with the set of support images. In particular, the memory augmented domain adaptation platform  114  is configured to compute a set of features and/or a set of masks corresponding to the set of support images and provide the set of features and/or set of masks as additional input to the machine-learnt model  106  to adapt the machine-learnt model  106  to the target domain. Consequently, the machine-learnt model  106  “adapts” or “learns” the target domain using the small set of support images. 
     Moreover, the memory augmented domain adaptation platform  114  aids in the generation of an output by the machine-learnt model  106  based at least on the set of features and/or set of masks corresponding to the set of support images. Furthermore, the memory augmented domain adaptation platform  114  is configured to provide the output to facilitate analysis. Also, the output generated may be based on the task performed by the machine-learnt model  106 . For example, if the machine-learnt model  106  is configured to classify the input image, the output may be a binary value. However, if the machine-learnt model  106  is configured to segment the input image, the output may be an image corresponding to the segmented region(s) of interest. Moreover, in one example, the output may be visualized on an interface unit such as a display  120 . 
     Furthermore, as illustrated in  FIG.  1   , the medical imaging system  108  may include the display  120  and a user interface  122 . In certain embodiments, such as in a touch screen, the display  120  and the user interface  122  may overlap. Also, in some embodiments, the display  120  and the user interface  122  may include a common area. In accordance with aspects of the present specification, the display  120  of the medical imaging system  108  may be configured to display or present the output generated by the machine-learnt model  106 . Moreover, any quality metrics/indicators generated by the memory augmented domain adaptation platform  114  may also be visualized on the display  120 . 
     In addition, the user interface  122  of the medical imaging system  108  may include a human interface device (not shown) configured to aid the clinician in manipulating image data displayed on the display  120 . The human interface device may include a mouse-type device, a trackball, a joystick, a stylus, or a touch screen configured to facilitate the clinician to identify the one or more regions of interest in the images. However, as will be appreciated, other human interface devices, such as, but not limited to, a touch screen, may also be employed. Furthermore, in accordance with aspects of the present specification, the user interface  122  may be configured to aid the clinician in navigating through the acquired images and/or output generated by the medical imaging system  108 . Additionally, the user interface  122  may also be configured to aid in manipulating and/or organizing the displayed images and/or generated indicators displayed on the display  120 . 
     Implementing the imaging system  100  that includes the memory augmented domain adaptation platform  114  as described hereinabove aids in enhancing the performance of the machine-learnt model  106  when the model  106  is deployed in a new target domain In particular, the memory augmented domain adaptation platform  114  aids in facilitating the adaptation of the machine-learnt model  106  to the target domain via use of the small set of support images. Additionally, the memory augmented domain adaptation platform  114  provides continuous learning to the machine-learnt model  106  via use of the set of support images, thereby improving the performance of the machine-learnt model  106  when the model  106  is deployed in new target domains. 
     In the present specification, embodiments of exemplary methods of  FIGS.  2 - 3    may be described in a general context of computer executable instructions on a computing system or a processor. Generally, computer executable instructions may include routines, programs, objects, components, data structures, procedures, modules, functions, and the like that perform particular functions or implement particular abstract data types. 
     Additionally, embodiments of the exemplary methods of  FIGS.  2 - 3    may also be practiced in a distributed computing environment where optimization functions are performed by remote processing devices that are linked through a wired and/or wireless communication network. In the distributed computing environment, the computer executable instructions may be located in both local and remote computer storage media, including memory storage devices. 
     Further, in  FIGS.  2 - 3   , the exemplary methods are illustrated as a collection of blocks in a logical flow chart, which represents operations that may be implemented in hardware, software, or combinations thereof. The various operations are depicted in the blocks to illustrate the functions that are performed. In the context of software, the blocks represent computer instructions that, when executed by one or more processing subsystems, perform the recited operations. 
     The order in which the exemplary methods of  FIGS.  2 - 3    are described is not intended to be construed as a limitation, and any number of the described blocks may be combined in any order to implement the exemplary methods disclosed herein, or equivalent alternative methods. Additionally, certain blocks may be deleted from the exemplary methods or augmented by additional blocks with added functionality without departing from the spirit and scope of the subject matter described herein. Although, the exemplary embodiments illustrated hereinafter are described in the context of a medical imaging system, it will be appreciated that use of the systems and methods in industrial applications is also contemplated in conjunction with the present specification. 
     Referring now to  FIG.  2   , a flow chart  200  depicting an exemplary method for adapting a machine-learnt model to a target domain is presented. The method  200  of  FIG.  2    is described with reference to the components of  FIG.  1   . In one embodiment, the method  200  may be performed by the memory augmented domain adaptation platform  114  in conjunction with the machine-learnt model  106 . 
     The method includes receiving an input image, when the imaging system  100  and the machine-learnt model  106  in particular is deployed in the target domain, as indicated by step  202 . The input image corresponds to a target domain Also, the input image may be received by the machine-learnt model  106  and the memory augmented domain adaptation platform  114 . 
     Further, at step  204 , one or more features corresponding to the input image are computed, by the memory augmented domain adaptation platform  114 . These features may include texture features, shape features, or a combination thereof. Some non-limiting examples of the texture features include wavelet features, machine-learnt features and the like. Also, some non-limiting examples of the shape features include contour-based features, features derived from dictionary-based approaches, moments, shape representations such as area, tangent angles, contour curvature, shape transform domain features such as Fourier transforms, and the like. Also, as previously noted, some examples of other features include age, gender, EMR information of the patient, and the like. 
     It may be noted that images corresponding to the target domain may be stored in the memory unit  118 . Subsequent to the computation of the image features corresponding to the input image, a set of support images may be identified by the memory augmented domain adaptation platform  114  based on the image features of the input image, as indicated by step  206 . In particular, the memory augmented domain adaptation platform  114  is configured to query the memory unit  118  using the image features corresponding to the input image to identify the set of support images from the images stored in the memory unit  118 . It may be noted that the set of support images is a subset of the images corresponding to the target domain In one non-limiting example, the set of support images includes images in a range from about three images to about five images. Further, in a scenario where the query to the memory unit  118  fails to identify one or more support images, one or more images corresponding to the source domain may be used as the support images. 
     Conventional machine-learning techniques generate an output based solely on the received input image, thereby leading to degradation of performance of the machine-learnt model in a new domain. In accordance with aspects of the present specification, the shortcomings of the presently available techniques are circumvented via use of the retrieved set of support images. The set of support images is used by machine-learnt model  106  to provide a context for the prediction as opposed to using only the input image. 
     More particularly, the method includes augmenting an input to the machine-learnt model with a set of features and/or set of masks corresponding to the set of support images to adapt the machine-learnt model to the target domain, as depicted by step  208 . Accordingly, the memory augmented domain adaptation platform  114  is configured to compute one or more features and/or masks corresponding to the retrieved set of support images. In one example, a tunable feature extraction suited for segmentation may be used to compute the features corresponding to the set of support images. In another example, a support context vector augmentation may be used to compute the features corresponding to the set of support images. In yet another example, the features corresponding to the set of support images may be computed by mimicking settings corresponding to a target domain during the training phase of the machine-learnt model  106  with data corresponding to the source domain. 
     As previously noted, these features may include texture features and/or shape features corresponding to the set of support images. Subsequently, the memory augmented domain adaptation platform  114  provides the features and/or masks corresponding to the set of support images as additional input to a predictor of the machine-learnt model  106 , thereby augmenting the input to the machine-learnt model  106 . Moreover, other features may also be provided as additional input to the predictor of the machine-learnt model  106 , as previously noted. 
     Furthermore, at step  210 , the machine-learnt model  106  is configured to generate an output based at least on the set of features and/or set of masks corresponding to the set of support images provided by the memory augmented domain adaptation platform  114  and the input image. The output generated by the machine-learnt model  106  may vary based on the task performed by the machine-learnt model  106 . By way of example, if the machine-learnt model  106  is used to perform a classification task, the output generated by the machine-learnt model  106  may be a binary value. In a similar fashion, if the machine-learnt model  106  is used to perform a segmentation task, the output generated by the machine-learnt model  106  may be a mask or a segmented image. 
     Furthermore, the output may be utilized to facilitate analysis, as indicated by step  212 . By way of example, the memory augmented domain adaptation platform  114  may be configured to visualize the mask or segmented image and/or the binary value generated by the machine-learnt model  106  on the display  120 . In certain embodiments, a visual comparison the performance of the system  100  with and without domain adaptation may be visualized on the display  120  to aid the clinician in any diagnosis or analysis. Additionally, any metrics associated with the output may also be visualized on the display  120 . In certain embodiments, the metrics may be superimposed on a corresponding output on the display  120 . 
     In another example, the memory augmented domain adaptation platform  114  may also be configured to communicate the generated output to a user such as a clinician or another system. The clinician and/or another system may use the output to facilitate a diagnosis and/or an analysis. The method  200  will be described in greater detail with reference to  FIG.  3   . 
     Turning now to  FIG.  3   , a schematic representation  300  illustrating the exemplary method for memory augmented domain adaptation of  FIG.  2    is depicted. Also,  FIG.  3    is described with reference to the components of  FIG.  1 - 2   . 
     As previously noted, the machine-learnt model  106  is typically trained using a dataset corresponding to a source domain. Once the machine-learnt model  106  is deployed in a new target domain, the exemplary memory augmented domain adaptation platform  114  is configured to adapt the machine-learnt model  106  to the target domain using a relatively small set of support images. In one non-limiting example, the set of support images may include images in a range from about three images to about five images. 
     Further, as depicted in  FIG.  3   , an input image  302  corresponding to a target domain is received by a machine-learnt model  106  and the memory augmented domain adaptation platform  114 . Subsequently, the memory augmented domain adaptation platform  114  computes one or more features  304  corresponding to the input image  302 . These features include texture features, shape features, and/or other features. 
     Moreover, the memory augmented domain adaptation platform  114  is configured to identify a set of support images corresponding to the target domain using the computed features associated with the input image  302 . Accordingly, the memory augmented domain adaptation platform  114  is configured to query the memory unit  118  based on the features corresponding to the input image  302  to identify existence of a match in the memory unit  118 . Specifically, the memory augmented domain adaptation platform  114  is configured to identify a set of support images based on the one or more image features corresponding to the input image  302 . The set of support images corresponds to the target domain and include features that match the features of the input image  302 . 
     It may be noted that in a classical U-Net, given training pairs of images and segmentation masks {I k , S k }, k=1, 2, . . . , N, a framework learns a predictor  [·] defined by parameters w that minimizes a training loss. One such example is presented in equation (1). 
     
       
         
           
             
               
                 
                   RMSE 
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                           k 
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     where  [·] is a learnt predictor and is a composition of an encoder and decoder D w ·E w . 
     In accordance with aspects of the present specification, a memory unit M such as the memory unit  118  is provided. The memory unit M  118  is defined by a matrix T N×Ft , where F t  is a feature length representing the texture features and a matrix G N×Fs , where F s  is a feature length representing the shape features. In one example, the memory unit M  118  is defined as:
 
 M =( T   N×Ft   , G   N×Fs )   (2)
 
     In response to the query, for every input image I k , the memory unit M  118  is configured to return a set of support images S(I k )  306 . One example of the set of support images S(I k )  306  is presented in equation (3).
 
 S ( I   k )= p   t , {t=1, 2, . . . , T}  (3)
 
In one example, in equation (1), T is a constant and is representative of a number of images in the set of support images S(I k )  306 . As previously noted, the set of support images S(I k )  306  corresponds to the target domain and is a subset of images corresponding to the target domain. In one non-limiting example, the set of support images includes images in a range from about three images to about five images. However, use of other number of images in the set of support images is also contemplated.
 
     For a given image I k , the set of support images S(I k )  306  is computed as:
 
( s   1   , s   2   , . . . , s   T )=NN T (q(I k ),  M )   (4)
 
where q is a feature corresponding to the input image I k  and NN T  are the T nearest neighbors for the given input q(I k ).
 
     The nearest neighbor operator NN is defined as:
 
 A=NN ( I   k   , M )=argmax i   q ( I   k )· q ( M   i )   (5)
 
     The memory unit  118  functions like a programmable memory and is used to facilitate adaptation and continuous learning of the machine-learnt model  106  to the target domain using a small set of support images corresponding to the target domain. Hence, when a similar case is subsequently encountered by the machine-learnt model  106 , the memory unit  106  is queried for a match. More particularly, the additional memory unit  118  facilitates retrieval of support images/samples similar to a particular use-case and the corresponding annotations. 
     In addition, the memory augmented domain adaptation platform  114  is configured to augment an input to the machine-learnt model  106  with the set of support images S(I k )  306 . The set of support images S(I k )  306  is used by machine-learnt model  106  to provide a context for the prediction as opposed to using only the input image. More particularly, the memory augmented domain adaptation platform  114  is configured to compute a set of features and/or set of masks  308  corresponding to the set of support images S(I k )  306 . As previously noted with reference to  FIG.  2   , different methods may be used to compute the features/masks  308  corresponding to the set of support images S(I k )  308 . 
     Further, the memory augmented domain adaptation platform  114  is configured to provide the set of features and/or set of masks  308  corresponding to the set of support images S(I k )  306  as additional input to the machine-learnt model  106  to adapt the machine-learnt model  106  to the target domain More particularly, in the machine-learnt model  106  (for example, a support augmented neural network), the input to a predictor {circumflex over (F)} of the machine-learnt model  106  is augmented with a set of texture and/or shape features and set of masks  308  corresponding to the set of support images S(I k )  306 . Moreover, the set of support images S(I k )  306  is used by the machine-learnt model  106  to provide a context for the prediction as opposed to using only the input image  302 . The texture and shape features are defined by:
 
Texture Features T: I k →T Ft×1    (6)
 
Shape Features G: S k →T Fs×1    (7)
 
     More particularly, the input to the decoder of the predictor F is changed to be a combination of the learnt encoded input E(I k ) and the shape and/or texture features and/or masks  308  corresponding to the set of support images S(I k )  306 . By way of example, the memory augmented domain adaptation platform  114  is configured to provide as input to the machine-learnt model  106  one or more machine-learnt features, one or more hard-coded features, masks  308  corresponding to the set of support images S(I k )  306 , or combinations thereof. 
     Additionally, the machine-learnt model  106  is configured to perform a desired task, such as but not limited to, a segmentation task and a classification task. Accordingly, the machine-learnt model  106  is configured to generate a modified output  310  based at least on the set of features and/or set of masks  308  corresponding to the set of support images S(I k )  306 . In one example, the output  310  includes a segmented image/mask, a binary value, or a combination thereof  312 . Also, in one example, the modified output  310  generated by the machine-learnt model  106  is represented as:
 
 = D ·( E [ I   k ]   T [ I   k ]   G [ I   k ])   (8)
 
where   is an operator such as, but not limited to, concatenation, average, sum, and the like and is used to combine the different features and/or masks.
 
     The masks/images and/or binary values  312  generated as output by the machine-learnt model  106  are communicated to the memory augmented domain adaptation platform  114 ). Additionally, the memory augmented domain adaptation platform  114  is configured to facilitate continuous learning of the machine-learnt model  106 . Accordingly, the memory augmented domain adaptation platform  114  is configured to verify validity of the output  310  (step  314 ). At step  314 , if it is verified that the output  310  is valid, the memory unit  118  is updated to store the output  310  (step  316 ). In particular, at step  316 , one or more features and/or one or more masks  320  corresponding to the set of support images S(I k )  306  are stored in the memory unit  118 . It may be noted that the features and/or masks  320  are tuned for performing tasks such as, but not limited to classification and segmentation. 
     However, at step  314 , if the validity of the output  310  is not verified, one or more annotations corresponding to the set of support images S(I k )  306  may be revised to generate a set of revised support images (step  318 ). Subsequently, the memory unit  118  is updated with the set of revised support images and features and/or masks corresponding to the set of revised support images. This validation and updating of the memory unit  118  aids in facilitating the continuous learning of the machine-learnt model  106 , thereby enhancing the domain adaptation capability of the machine learnt model  106 . 
     In certain embodiments, the memory augmented domain adaptation platform  114  may also be configured to update the memory unit  118  to optimize the memory unit M  118 . By way of example, the memory augmented domain adaptation platform  114  may delete one or more support images of the set of support images S(I k )  306  based on relevance of the set of support images S(I k )  306  to optimize the memory unit M  118 , while enabling enhanced performance of the imaging system  100 . By way of example, to track the relevance of a support image  306 , the memory augmented domain adaptation platform  114  may be configured to monitor the number of times that support image  306  is used for prediction, check the age of the support image  306  in the memory unit  118 , determine the similarity of the support image  306  to other images in the memory unit  118 , and the like. 
     Furthermore, the memory augmented domain adaptation platform  114  is configured to provide or communicate the output  310  to facilitate analysis (step  322 ). In one non-limiting example, the output  310  may be visualized on an interface unit such as the display  120 . The output  310  may be used for providing a diagnosis or for further analysis. 
     Implementing the memory augmented domain adaptation platform  114  as described hereinabove aids in adapting the machine-learnt model  106  to the target domain while circumventing the need for retraining the machine-learnt model  106 . In addition, since the machine-learnt model  106  is adapted to the target domain using a small set of support images  306 , the need for a voluminous dataset corresponding to the target domain to retrain the machine-learnt model  106  is obviated. Moreover, the machine-learnt model  106  is continuously trained using the set of support images  306  that is stored in the memory unit  118  and provided by the memory augmented domain adaptation platform  114 , thereby further enhancing the adaptation of the machine-learnt model  106  to the target domain. 
     Referring now to  FIGS.  4 ( a )- 4 ( c ) , diagrammatical representations of different datasets  402 ,  404 ,  406  corresponding to different domains are presented. In particular, these datasets  402 ,  404 ,  406  correspond to different domains with subjects having different diseases to simulate a deployment scenario of a machine-learnt model. Also,  FIGS.  4 ( a )- 4 ( c )  are described with reference to the components of  FIG.  1 - 3   . It may be noted that in the present example, the datasets  402 ,  404 ,  406  include X-ray images corresponding to three different domains. Additionally, the samples such as the X-ray images corresponding to the three datasets  402 ,  404 ,  406  have variations in texture, disease conditions, and gender. In the example depicted in  FIGS.  4 ( a )- 4 ( c ) , use of the machine-learnt model to perform a lung segmentation task from X-ray images is represented. As will be appreciated, lung segmentation is considered a challenging task owing to variations in the lung due to variations in anatomy, diseases, and the like amongst different patients. The three datasets  402 ,  404 ,  406  are used to understand the effect of changes in domains on the performance of the machine-learnt model. A U-Net is employed as a base learning model. 
       FIG.  4 ( a )  depicts a first dataset  402 . In the present example, the first dataset  402  is a Montgomery TB dataset. The Montgomery TB dataset  402  is an open source NHS dataset and includes 138 posterior-anterior X-ray images. Of these images, 80 X-ray images are indicative of normal data and 58 X-ray images are representative of abnormal data with manifestations of tuberculosis. Moreover, in the present example, the Montgomery TB dataset  402  is a source dataset used to train the machine-learnt model  106 . 
     Further,  FIG.  4 ( b )  depicts a second dataset  404 . The second dataset  404  is a GE pneumoconiosis dataset. The GE pneumoconiosis dataset  404  includes  330  images with lung mask annotations. It may be noted that pneumoconiosis is an occupational lung disease that typically affects factory workers and early stages of the diseases can be detected by dust and other metal settlements in the lungs. In the present example, the GE pneumoconiosis dataset  404  corresponds to a first target domain. 
     Moreover,  FIG.  4 ( c )  illustrates a third dataset  406 . The third dataset  406  is a Japanese Society of Radiological Technology (JSRT) dataset. The JSRT dataset  406  includes chest X-rays with lung nodules. Also, the JSRT dataset  406  includes 247 images. In the present example, the JSRT dataset  406  corresponds to a second target domain 
     In addition, to demonstrate the effectiveness of use of the memory augmented domain adaptation platform  114 , the machine-learnt model  106  is trained using samples obtained exclusively from the Montgomery TB dataset  402 . Once the machine-learnt model  106  is trained using samples from the Montgomery TB dataset  402 , the machine-learnt model  106  is deployed in new target domains to test the adaptability of the machine-learnt model  106  to the target domains using samples from corresponding target domains In one example, the trained machine-learnt model  106  is deployed in new target domains and the domain adaptation of the machine-learnt model  106  is tested using samples corresponding to the GE pneumoconiosis dataset  404  and the JSRT dataset  406 . The results are validated using a Dice score. 
     Table 1 presents a comparison of domain adaptation performance of various machine-learnt models using the datasets  402 ,  404 ,  406  of  FIGS.  4   a   )- 4 ( c ). Column 1 of Table 1 lists the techniques used in the comparative study. Further, column 2 of Table 1 corresponds to the source dataset  402  (Montgomery TB dataset). Also, column 3 of Table 1 corresponds to the first target dataset  404  (GE pneumoconiosis dataset). Column 4 of Table 1 corresponds to the second target dataset  406  (JSRT dataset). Moreover, row 1 of Table 1 corresponds to the performance of a base technique or learning model (U-Net) in the three domains  402 ,  404 ,  406 . Similarly, a second row of Table  1  of corresponds to the performance of the method for adapting the machine-learnt model  106  (SupportNet) described hereinabove in the three domains  402 ,  404 ,  406 . It may be noted that for the results presented in in row 2 of Table 1, the augmented input provided by the memory augmented domain adaptation platform  114  to the machine-learnt model  106  includes only the set of features corresponding to the set of support images. Additionally, row 3 of Table 1 corresponds to the performance of the present method (SupportNet) in the three domains  402 ,  404 ,  406 . It may be noted that in row 3 of Table 1, the augmented input provided by the memory augmented domain adaptation platform  114  to the machine-learnt model  106  includes the set of features and/or set of masks corresponding to the set of support images. 
     
       
         
           
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Technique 
                 Montgomery 
                 Pneumoconiosis 
                 JSRT 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                 U-Net 
                 0.968 
                 0.882 
                 0.896 
               
               
                 SupportNet using a support 
                 0.958 
                 0.910 
                 0.918 
               
               
                 set of features 
               
               
                 SupportNet using a support 
                 0.959 
                 0.947 
                 0.964 
               
               
                 set of features and masks 
               
               
                   
               
            
           
         
       
     
     Moreover, the results presented in Table 1 are obtained using a Dice score of 1 as a metric. Based on the results presented in Table 1, it may be deduced that the performance of the SupportNet (see rows 2 and 3) that uses the augmented input of support features and support masks is better than the performance of the U-Net (see row 1) on images obtained from different cohorts/domains. Accordingly, the SupportNet enjoys better generalization than the U-Net. 
     Turning now to  FIGS.  5 ( a )- 5 ( e ) , diagrammatic illustrations of a comparison of results of the domain adaptation performance of different machine-learnt models presented in Table  1  to perform a desired task are depicted. In the example presented in  FIGS.  5 ( a )- 5 ( e ) , it desirable to use the machine-learnt model  106  to segment the lung region in the input image  502 . Also,  FIGS.  5 ( a )- 5 ( e )  are described with reference to the components of  FIGS.  1 - 4   . 
       FIG.  5 ( a )  represents an input image  502  such as the input image  302  of  FIG.  3   . Further,  FIG.  5 ( b )  represents a ground truth mask  504 . In one example, the image  504  may include ground truth annotation by a clinician. 
     Also,  FIG.  5 ( c )  represents a segmented image  506  generated using a baseline technique such as the U-net without domain adaptation. Similarly,  FIG.  5 ( d )  represents a segmented image  508  generated by the SupportNet using only features corresponding to a set of support images. Also,  FIG.  5 ( e )  represents a segmented image  510  generated by the SupportNet using features and masks corresponding to a set of support images. 
     It may be noted that using only the features from the set of support images corresponding to the nearest neighbors aids the SupportNet with a suitable feature prior. In addition, when the features from the set of support images are augmented with the masks from the set of support images, the machine-learnt model achieves the dual objectives of balancing the shape as well as providing high fidelity to the images. 
     Embodiments of the present systems and methods for memory augmented domain adaptation advantageously present a continuous learning-based technique to adapt a machine-learnt model to a target domain to deliver better performance with a small set of samples. Moreover, the output generated by present systems and methods facilitates enhanced understanding of the predictions by comparing a set of similar examples used for arriving at a decision. Moreover, the systems and methods enable domain adaptation and continuous learning of the machine-learnt model without the constraint of requiring a large dataset of target domain samples during a development phase of the technique. In particular, the systems and methods enable domain adaptation with a very small set of samples. 
     Additionally, the systems and methods entail use of a “meta-learning” technique designed to enable improvements in the performance of the machine-learnt model when deployed in new domains. In particular, the memory unit is used to facilitate adaptation and continuous learning of the machine-learnt model to a target domain using a small set of samples corresponding to the target domain. Accordingly, when a similar case is subsequently encountered by the machine-learnt model, the memory unit is queried for a match. More particularly, the additional memory unit facilitates retrieval of samples similar to a particular use-case and the corresponding annotations. Furthermore, the annotations corresponding to the retrieved samples stored in the memory unit may be revised, thereby providing enhanced control over the subsequent predictions by the machine-learnt model. 
     It may be noted that the foregoing examples, demonstrations, and process steps that may be performed by certain components of the present systems, for example by the processing subsystem  112  and the memory augmented domain adaptation platform  114  in particular, may be implemented by suitable code on a processor-based system. The processor-based system, for example, may include a general-purpose or a special-purpose computer. It may also be noted that different implementations of the present specification may perform some or all of the steps described herein in different orders or substantially concurrently. 
     Additionally, the functions may be implemented in a variety of programming languages, including but not limited to Ruby, Hypertext Preprocessor (PHP), Perl, Delphi, Python, C, C++, or Java. Such code may be stored or adapted for storage on one or more tangible, machine-readable media, such as on data repository chips, local or remote hard disks, optical disks (that is, CDs or DVDs), solid-state drives, or other media, which may be accessed by the processor-based system to execute the stored code. 
     Although specific features of embodiments of the present specification may be shown in and/or described with respect to some drawings and not in others, this is for convenience only. It is to be understood that the described features, structures, and/or characteristics may be combined and/or used interchangeably in any suitable manner in the various embodiments. 
     While only certain features of the present disclosure have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.