The present disclosure describes a system and method for classification determination of a structure. The method may include obtaining images that represent a the structure of an object. This technique can also be utilized to identify various possible classifications for the structure, and their probabilities, by putting the data in an appropriate classification model. A backbone network is utilized to determine a structure’s backbone feature as well as a segmentation system is utilized to determine a structure’s segmentation feature. The model for classification also includes a density-based classification network that is used to determine a structure’s density feature. The goal classification for the structure could be determined using at least one of the probabilities derived from the plurality of possible classifications.

Medical imaging for diagnosis and treatment may be implemented by systems including, e.g., a X-ray imaging system, a positron emission tomography (PET) system, a magnetic resonance (MR) system, a computed tomography (CT) system, a single-photonemission computed tomography (SPECT) system, a radioisotope imaging system, etc. CT is an example of a frequently utilized diagnostic method. However, image analysis by a healthcare provider, e.g., a doctor, may be time consuming, and/or introduceinconsistencies or errors caused by differences between healthcare providers. Computer-aided diagnosis (CAD) is a method that has been used to improve diagnostic accuracy and reduce the workload of doctors, was developed in recent years. However, structures, suchas nodules pleural or pulmonary of small sizes are not yet able to distinguish their classfications (e.g. malignancies). Therefore, it is desirable to develop the system and procedure that automates the classification of structures.

A system is described in the first aspect of the disclosure. The system can comprise at least one storage media including a set of instructions, as well as at least one processor that is connected to the storage medium. Whenexecuting the set of instructions, the at least one processor may be configured to direct the system to perform operations including obtaining image data representing a structure of a subject, determining a plurality of candidate classifications of thestructure and their respective probabilities by inputting the image data into a classification model, wherein the classification model includes a backbone network for determining a backbone feature of the structure, a segmentation network for determininga segmentation feature of the structure, and a density classification network for determining a density feature of the structure, and determining a target classification of the structure based on the probabilities of the plurality of candidateclassifications.

In some embodiments, the backbone network can include several down-sampling layers. Each down-sampling layer may include a convolution layer and a batch normalization.

In certain embodiments, determining a plurality of candidate classifications of the structure inputting the image data into a classification model might include obtaining the backbone feature the segmentation feature and the density feature, by inputting the image data into the backbone network, the segmentation network, or the density classification network in turn, and then the determination of a probability for each of the many candidate classifications of the structure based on the backbone feature, the segmentation feature, and the density feature.

In certain methods, the process of determining the probability that every structure is classifiable according to its backbone feature and segmentation feature, as well as the density feature may also include the identification featureof images. This will combine the backbone feature, segmentation and density features and determine the probability of every structure being classified based on the identification feature that is identified in image data.

In some embodiments, the determination of an identification feature of the image data combing the backbone feature, the segmentation feature and the density feature could involve converting the backbone features as well as the segmentation feature and the density feature into a one-dimensional backbone feature vector as well as a one-dimensional segmentation vector and a one-dimensional feature vector, respectively, making a one-dimensional identification vector by splicing the one-dimensional backbone feature vector as well as the one-dimensional segmentation vector as well as the one-dimensional feature vector, then identifying the one-dimensional identification feature vector as the identifying feature of the image data.

Certain models allow the model of classification to be trained by using the focal loss function. At at least one weight in every focal loss function is related to one candidate classification. The focal loss function is related to the other number of.

In some embodiments, the process of obtaining images that represent the shape of a subject might consist of obtaining original image data including an image of the structure of the subject, and determining the image data by preprocessing the original image data.

In some embodiments, the determining the image data by processing the original image data might include creating a resampled picture by resampling the original image data in accordance with a resampling resolution, segmenting the image cropped in accordance with a central point of the structure, and determining the image data using normalization of the crop sizes of the image in accordance with a normalizing function.

A second aspect of the disclosure provides an apparatus. The system may comprise at least 1 storage medium, a set of instructions, and at most one processor communicating with the storage medium. The at least one processor is able to direct the system to perform the instructions. This includes obtaining the preliminary class model as well as developing a classification model in order to determine a pluralityofcandidate classifications of a subject illustrated in images. Each of these tasks can be accomplished by training the initial model with a focal loss function. A minimum of one weight of every focal loss function is related to one of several possible classifications.

A third aspect of this disclosure is a method. This method can be applied to a computing device that has at least one processor, as well as at least one computer readable storage device. The method may include obtaining, by thecomputing device, image data representing a structure of a subject, determining, by the computer, a plurality of candidate classifications of the structure as well as their probabilities of each by entering the data into a classification model, wherein the classification model includes the backbone network that determines the backbone of the structure and a segmentation network for determining a segmentation feature of the structure as well as a density classification network that determines a density feature of the structure and determining, using the computer, a target classification of the structure based on the probabilities of the various candidates for classification.

Additional characteristics will be explained in part in the following description. Parts of these features will be obvious to experienced art professionals when they look at the following and the drawings that accompany it. Other features could be made evident by the production of or operation of the examples. The advantages of this disclosure can be accomplished and achieved through practice or use of various aspects of the methods instruments, combinations and methods described in the specific examples below.

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Begin by writing down a concise detailed description of your idea. Do not use generic terms such as “device,” “process,” and “system.” Instead, think about synonyms for the terms you selected initially. Also, make note of key technical terms as well as keywords.

To help you find keywords and concepts, use the questions below.

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2. Use these terms to find relevant Cooperative Patent Classifications on the Classification Text Search Tool. If you’re not able to locate the appropriate classification to describe your invention, look through the classification’s Schemas of classes (class schedules) and try again. Consider substituting the words that you’ve used to describe your invention if you do not get any results from the Classification Text Search with synonyms such as the terms you used in step 1.

3. Check the CPC Classification Definition to confirm the validity of the CPC classification you found. The link to the CPC classification definition is available when the classification you have selected is a blue square with a “D” to its left. CPC classification definitions will help determine the relevant classification’s scope and therefore you’re certain to choose the one that is relevant. The definitions could also contain search tips or other suggestions that could be helpful for further investigation.

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5. This collection of patent publications is the most appropriate to check for similarity to your invention. Pay close attention to the specification and claims. Consult the applicant and patent examiner for any additional patents.

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7. You can find other US patent publications using keywords in the AppFT and PatFT databases, as well as the classification search of patents that are that are not issued by the United States according to below. Also, you can make use of search engines on the internet to find non-patent literature disclosures about inventions. For instance:

  • Add keywords to your search. Keyword searches may turn up documents that are not well-categorized or have missed classifications during Step 2. For example, US patent examiners often supplement their classification searches with keyword searches. Think about the use of technical engineering terminology rather than everyday words.
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