Patent Description:
Binary files, such as images, animations, and sounds, have allowed for the creation and encoding of large amounts of data. For example, in the field of medicine, microscopy image data and molecular data are extensively used for cancer research. In this case, non-tabular microscopy image data provides information about changes in cell structure and patterns, while tabular molecular data provides information about changes in gene types.

Digital pathology has made it possible to extract information about biological components from whole slide images. Hematoxylin and eosin (H&E) is a common staining technique used in pathology labs across the world. In a typical implementation, hematoxylin dye stains nuclei by blue while eosin stains cytoplasm and extracellular matrix by pink. Cells and nuclei are the basic elements of tissue, and the statistics of such components can be utilized for novel biomarker development as well as for precise diagnosis. Nucleus size, shape, density, local texture, spatial features in the neighborhood of nucleus and local tissue structure (glands) provide important clues about cancerous activity in the whole slide images.

<CIT> relates to a system quantifying a density level of tumor-infiltrating lymphocytes, based on prediction of reconstructed TIL information associated with tumoral tissue image data during pathology analysis of the tissue image data. The system receives digitized diagnostic and stained whole-slide image data related to tissue of a particular type of tumoral data. Defined are regions of interest that represents a portion of, or a full image of the whole-slide image data. The image data is encoded into segmented data portions based on convolutional autoencoding of objects associated with the collection of image data. The density of tumor-infiltrating lymphocytes is determined of bounded segmented data portions for respective classification of the regions of interest. A classification label is assigned to the regions of interest. It is determined whether an assigned classification label is above a pre-determined threshold probability value of lymphocyte infiltrated. A refined TIL representation based on prediction of the TIL representations is generated using the adjusted threshold probability value associated with the classified segmented data portions.

Embodiments relate to a method, system, and computer readable medium for predicting features in binary files. This object is achieved by the subject-matter of the independent claims. The following paragraphs also include embodiments, aspects and examples that are not specifically claimed, but may be useful for understanding the invention. According to one aspect, a method for predicting features in binary files is provided. The method may include dividing an image into one or more patch images. Spatial features corresponding to the one or more patch images are compressed. Output data corresponding to the compressed spatial features is predicted. The output data is predicted based on minimizing one or more loss functions corresponding to the compressed spatial features.

According to another aspect, a computer system for predicting features in binary files is provided. The computer system may include one or more processors, one or more computer- readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, whereby the computer system is capable of performing a method. The method may include dividing an image into one or more patch images. Spatial features corresponding to the one or more patch images are compressed. Output data corresponding to the compressed spatial features is predicted. The output data is predicted based on minimizing one or more loss functions corresponding to the compressed spatial features.

According to yet another aspect, a computer readable medium for predicting features in binary files is provided. The computer readable medium may include one or more computer-readable storage devices and program instructions stored on at least one of the one or more tangible storage devices, the program instructions executable by a processor. The program instructions are executable by a processor for performing a method that may accordingly include dividing an image into one or more patch images. Spatial features corresponding to the one or more patch images are compressed. Output data corresponding to the compressed spatial features is predicted. The output data is predicted based on minimizing one or more loss functions corresponding to the compressed spatial features.

These and other objects, features and advantages will become apparent from the following detailed description of illustrative embodiments, which is to be read in connection with the accompanying drawings. The various features of the drawings are not to scale as the illustrations are for clarity in facilitating the understanding of one skilled in the art in conjunction with the detailed description. In the drawings:.

Detailed embodiments of the claimed structures and methods are disclosed herein; however, it can be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be embodied in various forms. Those structures and methods may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and more particularly to machine learning. The following described exemplary embodiments provide a system, method and computer program to, among other things, predict molecular data based on H&E image data. Therefore, some embodiments have the capacity to improve the field of computing by allowing for RNA data to be predicted by a computer using readily available, simple, and inexpensive diagnostic methods.

As described above, binary files, such as images, animations, and sounds, have allowed for the creation and encoding of large amounts of data. For example, in the field of medicine, microscopy image data and molecular data are extensively used for cancer research, and digital pathology has made it possible to extract information about biological components from whole slide images. Hematoxylin and eosin (H&E) is a common staining technique used in pathology labs across the world, to assess nucleus size, shape, density, local texture, spatial features in the neighborhood of nucleus and local tissue structure (glands). These features provide important clues about cancerous activity in the whole slide images. In cancer diagnosis and treatment, molecular profiling of patients is in increasing demand to take advantage of targeted or biomarker-based therapies. For example, patients with lung cancer who have EGFR mutations or patients with melanoma who have BRAF mutations have received approval for targeted therapies by the US Food and Drug Administration.

However, measuring molecular data is a very costly and time-consuming clinical procedure due to expensive equipment, long analysis time and tissue sample dependency. On the other hand, H&E techniques are inexpensive and may constitute a relatively simple procedure. Studies have shown that genetic changes in molecular data cause cell-structure changes in H&E data, and thus, the two data types are correlated and linked. It may be advantageous, therefore, to predict molecular data using features from captured microscopy image data (such as H&E image data).

Microscopy and medical image data, however, can be very large in size, resulting in difficulty in loading these images into analysis models, such as deep learning models trained to predict the molecular data (e.g., RNA data), due to computational constraints. One method of overcoming these constraints is patch-based extraction, in which a random sample of patches or tiles are obtained from the image and then analysis results are aggregated over those patches or tiles. For example, a deep learning model may be used to predict the molecular data from each patch (feature extraction), and a patch aggregator may thereafter aggregate (e.g., average) the results for all of the patches. Accordingly, full image prediction may be achieved using a deep learning model by dividing the image into patches for input to the model and then aggregating the results.

The patch-based approach has a number of limitations. First, the patches are extracted randomly from the image. In reality, however, molecular data is not uniform and the random sample of patches may not be an accurate reflection of the features across the entire image. If, for examples, one of the patches has excessive noise or does not have sufficient information, then the aggregated result would be erroneous and would not accurately reflect the molecular data (e.g., RNA data). Further, because the patches are extracted randomly from the image, the features therein are treated individually, and the spatial relationships between the patches are lost. The spatial relationships, however, are important for molecular data prediction, particularly for identifying important areas of the image in terms of the molecular data. Another problem is that the deep learning model for feature extraction may be pre-trained with images unrelated to medical or microscopy images. For example, a deep learning model trained with images of dogs or cars may be applied to medical images for feature extraction. Given the extreme differences between the datasets (images of dogs versus medical images), the feature extraction is not optimal.

Aspects of exemplary embodiments overcome these problems by incorporating a spatial feature compressor that retains the spatial relationships between extracted patches, and a deep learning regressor that inputs compressed features and outputs predicted molecular data. With this framework, the assumption that each patch has molecular data information is avoided; both the local cellular features and the global spatial features for inferring molecular data at the whole-slide level are used. Additionally, aspects of exemplary embodiments provide end-to-end training of the feature compressor and deep-learning regressor using combined loss, as opposed to a model pre-trained with unrelated datasets. As a result, the accuracy of the feature extraction and prediction of molecular data improves.

Aspects are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer readable media according to the various embodiments.

The following described exemplary embodiments provide a system, method and computer program that predicts data (e.g., molecular data) from an image (e.g., from H&E microscopy image data. While exemplary embodiments herein are described with reference to predicting molecular data from H&E microscopy data in humans, it is understood that the present disclosure is not limited thereto and may be applicable to predicting any type of data indicated by an image of any type, based on features extracted from the image.

Referring now to <FIG>, a functional block diagram of a networked computer environment illustrating a molecular data prediction system <NUM> (hereinafter "system") for predicting molecular data based on H&E images is provided. It should be appreciated that <FIG> provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

The system <NUM> may include a computer <NUM> and a server computer <NUM>. The computer <NUM> may communicate with the server computer <NUM> via a communication network <NUM> (hereinafter "network"). The computer <NUM> may include a processor <NUM> and a software program <NUM> that is stored on a data storage device <NUM> and is enabled to interface with a user and communicate with the server computer <NUM>. As will be discussed below with reference to <FIG> the computer <NUM> may include internal components 800A and external components 900A, respectively, and the server computer <NUM> may include internal components 800B and external components 900B, respectively. The computer <NUM> may be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing devices capable of running a program, accessing a network, and accessing a database.

The server computer <NUM> may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (laaS), as discussed below with respect to FIGS. <NUM> and <NUM>. The server computer <NUM> may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud.

The server computer <NUM>, which may be used for predicting features in binary files is enabled to run a Molecular Data Prediction Program <NUM> (hereinafter "program") that may interact with a database <NUM>. The Molecular Data Prediction Program method is explained in more detail below with respect to <FIG> and <FIG>. In one embodiment, the computer <NUM> may operate as an input device including a user interface while the program <NUM> may run primarily on server computer <NUM>. In an alternative embodiment, the program <NUM> may run primarily on one or more computers <NUM> while the server computer <NUM> may be used for processing and storage of data used by the program <NUM>. According to another embodiment, the program <NUM> may run on one or more computers <NUM> and data used by the program <NUM> may be stored in the one or more computers <NUM>. In this case, the server computer <NUM> may be omitted. It should be noted that the program <NUM> may be a standalone program or may be integrated into a larger molecular data prediction program.

Further, it should be noted that processing for the program <NUM> may, in some instances, be shared amongst the computers <NUM> and the server computers <NUM> in any ratio. In another embodiment, the program <NUM> may operate on more than one computer, server computer, or some combination of computers and server computers, for example, a plurality of computers <NUM> communicating across the network <NUM> with a single server computer <NUM>. In another embodiment, for example, the program <NUM> may operate on a plurality of server computers <NUM> communicating across the network <NUM> with a plurality of client computers. Alternatively, the program may operate on a network server communicating across the network with a server and a plurality of client computers.

The network <NUM> may include wired connections, wireless connections, fiber optic connections, or some combination thereof. In general, the network <NUM> can be any combination of connections and protocols that will support communications between the computer <NUM> and the server computer <NUM>. The network <NUM> may include various types of networks, such as, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, a telecommunication network such as the Public Switched Telephone Network (PSTN), a wireless network, a public switched network, a satellite network, a cellular network (e.g., a fifth generation (<NUM>) network, a long-term evolution (LTE) network, a third generation (<NUM>) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a metropolitan area network (MAN), a private network, an ad hoc network, an intranet, a fiber optic-based network, or the like, and/or a combination of these or other types of networks.

Additionally, or alternatively, a set of devices (e.g., one or more devices) of system <NUM> may perform one or more functions described as being performed by another set of devices of system <NUM>.

Referring now to <FIG>, a block diagram of a data prediction system <NUM> is depicted. For example, the data prediction system <NUM> may be a molecular data prediction system that predicts molecular data from a microscopy image, though it is understood that the present disclosure is not limited to any particular type of data or image. The data prediction system <NUM> may include, among other components, a pre-processing module <NUM>, a patch extraction module <NUM>, a spatial feature compression module <NUM>, and a deep learning regression module <NUM>.

The pre-processing module <NUM> (or pre-processor) processes input data <NUM> (e.g., microscopy image data such as an H&E image or an immunofluorescence image). The input data <NUM> may include, among other things, primary tumor samples for head and neck squamous cell carcinoma (HNSCC), for which both H&E whole slide images and RNA sequence data may be available. However, it may be appreciated that data corresponding to substantially any pathology may be used. HNSCC may include cancers of multiple sites in head and neck region. The pre-processing module <NUM> may apply pre-processing on the input data <NUM> by down-sampling the input data <NUM> (e.g., by a factor of four) and apply a threshold (e.g., Otsu threshold) to separate tissue regions from background regions. According to an embodiment, the pre-processing may include at least one of down-sampling to reduce image size, Otsu thresholding (e.g., to remove background from a tissue region), stain normalization to normalize stain colors across different images, and contrast enhancement to enhance image features (e.g., tissue structure and cells).

The patch extraction module <NUM> may extract patches from the pre-processed image data. By way of example, and not of limitation, the patches may be of size <NUM> pixels by <NUM> pixels, which may be a common patch size used in histopathology image analysis. The extracted patches may also include, among other things, location data from the corresponding tissue regions. The patch extraction module <NUM> (or patch extractor) may extract one or more patch images from the input data <NUM>. In the present embodiment, the patch extraction module <NUM> extracts patches of the entirety of the image, as opposed to a random sample of patches.

Because the patches correspond to the full image, their sizes may be too large to input into the feature extraction model. Thus, the spatial feature compression module <NUM> (or spatial feature compressor) may compress the patches into multiple features. After the input data <NUM> may be converted to patches, the spatial feature compression module <NUM> may extract features from each patch to represent a low-dimensional pixel space into a high-dimensional feature space. To extract features while retaining the spatial-contextual information, the spatial feature compression module <NUM> may employ a Neural Image Compression (NIC) technique for compressing the whole slide images. NIC may use a neural network to map patches into feature vectors and place each feature vector into an array that keeps the original spatial arrangement intact such that neighboring feature vectors in the array represent neighboring patches in the original whole slide images. Several neural networks architectures have been used in NIC. For example, a variational auto-encoder (VAE) may be used for medical image classification. The spatial feature compression module <NUM> may use a pre-trained VAE trained on H&E datasets for obtaining compressed feature representation. For example, the patches may be compressed using a plurality of filters corresponding to different features (e.g., <NUM> filters to output a stack of <NUM> images). In other words, according to the present exemplary embodiment, the image may be compressed though the features may be expanded. Spatial information and other useful information can thereby be preserved for the prediction model.

The deep learning regression module <NUM> (or deep learning regressor) inputs the compressed feature images and outputs predicted molecular data <NUM> for the full image. As compared to the random patch-based approach, more information is fed to the deep learning regression module <NUM> (including spatial relationships), thereby providing more accurate molecular data prediction results. Specifically, the deep learning regression module <NUM> may input the compressed feature representation of the H&E whole slide images as feature vectors and may generate RNA sequence information, such as gene expression values, as the predicted molecular data <NUM>. The feature vectors may be resized to a size of, for example, <NUM> pixels by <NUM> pixels with <NUM> input channels, which may correspond to the length of each feature vector. The deep learning regression module <NUM> may contain one or more convolutional layers of kernel size (<NUM>,<NUM>) with stride = <NUM> and padding = <NUM> each followed by a batch-normalization layer and a max-pooling layer with stride = <NUM> and padding = <NUM>. In addition, the deep learning regression module <NUM> may include one or more hidden fully-connected layers and an output fully-connected layer. The convolutional layers learn local patch-level and global image-level features through hierarchical learning process and the fully-connected layers regress the gene values based on the learnt features. The deep learning regression module <NUM> may optimize the loss function of the proposed deep learning model with mean-squared error (MSE) loss function which computes the mean of the summation of squared difference between true and predicted RNA sequence gene expression values over the whole slide images used during training.

Further, the molecular data prediction system <NUM> may include end-to-end training of the spatial feature compression module <NUM> and the deep learning regression module <NUM> using combined loss. For example, the regression loss function may be defined as Equation <NUM>: <MAT> the feature compression loss function may be defined as Equation <NUM>: <MAT> and
the combined loss function may be defined as Equation <NUM>: <MAT> where i is the ith patch of the image, N is a total number of patches from one image, xpred is the reconstructed patch, xtrue is the actual patch, ypred is the predicted value of molecular data at the image-level, and ytrue is the actual value of molecular data at the image-level.

According to one or more embodiments, parameters (e.g., weights) of the first model and the second model may be adjusted (or optimized) based on the first and second loss functions, i.e., a combined loss function. As a result, feature extraction is improved since the first model for feature extraction is trained using relevant images, e.g., H&E images, instead of unrelated datasets that are used generically to pre-train related art models. Additionally, features are optimized specifically for the relevant task (e.g., for the task of molecular data prediction in the above-described embodiments). This leads to improved accuracy of the prediction task. Without the combined loss according to embodiments, features may be optimized for unrelated tasks such as classification/segmentation, and may not be useful for an intended task, e.g., molecular data prediction. Further, by implementing a combined loss according to embodiments, training is faster since the tasks of both models are trained together in an end-to-end framework instead of sequentially or a single model or task at a time.

Referring now to <FIG>, a block diagram of a spatial feature compression module <NUM> as illustrated in <FIG>, according to one or more embodiments, is depicted. The spatial feature compression module <NUM> as depicted in <FIG> may be neural image compression based. The spatial feature compression module <NUM> may include, among other components, feature extraction modules 302A-C (or feature extractors), a spatial arrangement module <NUM> (or spatial arranger), and a deep learning model output module <NUM> (or a deep learning model). The feature extraction modules 302A-C may extract features from the received input H&E data <NUM>. The spatial arrangement module <NUM> may generate compressed images based on the spatial positions of the extracted features. The deep learning model output module <NUM> may predict and output RNA data <NUM> from the compressed images.

Referring now to <FIG>, a block diagram of a spatial feature compression module <NUM> as illustrated in <FIG>, according to one or more embodiments, is depicted. The spatial feature compression module <NUM> as depicted in <FIG> may be tumor region detection based. The spatial feature compression module <NUM> may include, among other things, a cancer detection module <NUM> (or cancer detector), feature extraction modules 314A-B (or feature extractors), spatial arrangement modules 316A-B (or spatial arrangers), and combination module <NUM> (or combiner). It may be appreciated that cancer patch and normal patches may have different spatial patterns and morphology in an H&E image. Thus, the cancer detection module <NUM> may allow for separate learning of such patterns in order to enhance a compressed representation of the input H&E data <NUM>. Accordingly, the feature extraction module 314A may extract features from cancer patches while the feature extraction module 314B may extract features from normal patches. The spatial arrangement modules 316A and 316B may generate compressed cancer patch features and compressed normal patch features, respectively. The combination module <NUM> may combine the compressed cancer and normal patch features and may output RNA data <NUM> based on a deep learning model.

Referring now to <FIG>, an operational flowchart illustrating the steps of a method <NUM> carried out by a program that predicts molecular data is depicted.

At <NUM>, the method <NUM> may include dividing an image into one or more patch images. In the present embodiment, the patch extraction module <NUM> extracts patches of the entirety of the image, as opposed to a random sample of patches. Thus, the image as a whole may be used for data prediction, thereby preserving more complete and more robust information (such as spatial relationships of the features within the image) resulting in a more accurate prediction.

At <NUM>, the method <NUM> may include compressing spatial features corresponding to the one or more patch images. Here, a first model (e.g., a machine learning model such as a spatial feature compressor) may be used to compress feature expression images (e.g., compress H&E features) from the patch images.

At <NUM>, the method <NUM> may include predicting output data corresponding to the compressed spatial features. In particular, a second model (e.g., a machine learning model such as a deep learning regressor) may be used to predict the output data (such as molecular data or other feature data depending on the type of image).

According to one or more embodiments, a combined loss function may be used for end-to-end training of the system. That is, parameters of the first model and the second model may be adjusted or optimized based on a first loss function (e.g., Equation <NUM> above) of the first model and a second loss function (e.g., Equation <NUM> above) of the second model, i.e., a combined loss function. An example of the combined loss function is provided above with reference to Equation <NUM>. Thus, end-to-end training of the first model (feature compressor) and the second model (e.g., deep-learning regressor) is achieved using combined loss, as opposed to a model pre-trained with unrelated datasets. As a result, the accuracy of the feature extraction and prediction of the data (e.g., molecular or RNA data) improves.

It may be appreciated that <FIG> provides only an illustration of one implementation and does not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

<FIG> is a block diagram <NUM> of internal and external components of computers depicted in <FIG> in accordance with an illustrative embodiment. It should be appreciated that <FIG> provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

Computer <NUM> (<FIG>) and server computer <NUM> (<FIG>) may include respective sets of internal components 800A,B and external components 900A,B illustrated in <FIG>. Each of the sets of internal components <NUM> include one or more processors <NUM>, one or more computer-readable RAMs <NUM> and one or more computer-readable ROMs <NUM> on one or more buses <NUM>, one or more operating systems <NUM>, and one or more computer-readable tangible storage devices <NUM>.

Processor <NUM> is implemented in hardware, firmware, or a combination of hardware and software. Processor <NUM> is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, processor <NUM> includes one or more processors capable of being programmed to perform a function. Bus <NUM> includes a component that permits communication among the internal components 800A,B.

The one or more operating systems <NUM>, the software program <NUM> (<FIG>) and the Molecular Data Prediction Program <NUM> (<FIG>) on server computer <NUM> (<FIG>) are stored on one or more of the respective computer-readable tangible storage devices <NUM> for execution by one or more of the respective processors <NUM> via one or more of the respective RAMs <NUM> (which typically include cache memory). In the embodiment illustrated in <FIG>, each of the computer-readable tangible storage devices <NUM> is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices <NUM> is a semiconductor storage device such as ROM <NUM>, EPROM, flash memory, an optical disk, a magneto-optic disk, a solid state disk, a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of non-transitory computer-readable tangible storage device that can store a computer program and digital information.

Each set of internal components 800A,B also includes a R/W drive or interface <NUM> to read from and write to one or more portable computer-readable tangible storage devices <NUM> such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. A software program, such as the software program <NUM> (<FIG>) and the Molecular Data Prediction Program <NUM> (<FIG>) can be stored on one or more of the respective portable computer-readable tangible storage devices <NUM>, read via the respective R/W drive or interface <NUM> and loaded into the respective hard drive <NUM>.

Each set of internal components 800A,B also includes network adapters or interfaces <NUM> such as a TCP/IP adapter cards; wireless Wi-Fi interface cards; or <NUM>, <NUM>, or <NUM> wireless interface cards or other wired or wireless communication links. The software program <NUM> (<FIG>) and the Molecular Data Prediction Program <NUM> (<FIG>) on the server computer <NUM> (<FIG>) can be downloaded to the computer <NUM> (<FIG>) and server computer <NUM> from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces <NUM>. From the network adapters or interfaces <NUM>, the software program <NUM> and the Molecular Data Prediction Program <NUM> on the server computer <NUM> are loaded into the respective hard drive <NUM>. The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computer display monitor <NUM>, a keyboard <NUM>, and a computer mouse <NUM>. External components 900A,B can also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each of the sets of internal components 800A,B also includes device drivers <NUM> to interface to computer display monitor <NUM>, keyboard <NUM> and computer mouse <NUM>. The device drivers <NUM>, R/W drive or interface <NUM> and network adapter or interface <NUM> comprise hardware and software (stored in storage device <NUM> and/or ROM <NUM>).

It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, some embodiments are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

Some embodiments may relate to a system, a method, and/or a computer readable medium at any possible technical detail level of integration. The computer readable medium may include a computer-readable non-transitory storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out operations.

Computer readable program code/instructions for carrying out operations may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the "C" programming language or similar programming languages. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects or operations.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer readable media according to various embodiments. The method, computer system, and computer readable medium may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in the Figures. For example, two blocks shown in succession may, in fact, be executed concurrently or substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles "a" and "an" are intended to include one or more items, and may be used interchangeably with "one or more. " Furthermore, as used herein, the term "set" is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, etc.), and may be used interchangeably with "one or more. " Where only one item is intended, the term "one" or similar language is used. Also, as used herein, the terms "has," "have," "having," or the like are intended to be open-ended terms. Further, the phrase "based on" is intended to mean "based, at least in part, on" unless explicitly stated otherwise.

Claim 1:
A method of predicting data from an image, executable by a processor (<NUM>), comprising:
extracting, from an image, a plurality of patch images;
compressing, using a first model, spatial features corresponding to the plurality of patch images, the spatial features relating to the spatial relationship between the patch images; and
predicting, using a second model, output molecular data based on the compressed spatial features;
wherein the first model uses a neural network trained using biological images, and compressing the spatial features comprises:
detecting a pathology associated with the image;
identifying patch images corresponding to the detected pathology;
identifying normal patch images associated with the image;
determining spatial arrangements corresponding to the normal patch images and the patch images corresponding to the detected pathology; and
generating or training a deep learning model based on the determined spatial arrangements; and
wherein the second model is a deep learning regression model.