Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to artificial intelligence-based medical diagnostic systems, and particularly to an Artificial Intelligence (AI) powered histopathology analysis system for breast cancer detection.
Description of Related Art
[002] Breast cancer is one of the leading causes of cancer deaths worldwide. Early and accurate detection through histopathological analysis is critical to improving survival rates. However, manual inspection of biopsy slides by pathologists presents several challenges. The process is highly time-consuming, as pathologists must review thousands of slides daily, leading to delays in diagnosis and increased workload. It is also prone to human error, with variations in expertise and fatigue contributing to misdiagnoses, resulting in false positives or false negatives. Additionally, the dependency on highly trained professionals makes accurate diagnosis inaccessible in many healthcare facilities, particularly in resource-limited areas. Traditional histopathological methods lack scalability and lead to bottlenecks in the diagnostic process. Interpretation inconsistencies further impact reliability, as different pathologists may provide varying assessments for the same slide. While there are few digital pathology tools, however, these tools still require extensive manual intervention and lack full automation, limiting efficiency.
[003] Existing systems also fail to leverage AI-driven learning, missing opportunities for continuous improvement in diagnostic accuracy. Moreover, the high costs associated with sophisticated microscopy equipment and skilled professionals further restrict widespread adoption. Another critical limitation is the absence of real-time decision support, as current systems do not provide instant classification or risk assessment, delaying crucial treatment decisions. These challenges highlight the urgent need for an AI-powered histopathology analysis system that can enhance accuracy, efficiency, and accessibility in breast cancer detection.
[004] There is thus a need for an improved and advanced artificial intelligence-based histopathology analysis system for breast cancer detection that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[005] Embodiments in accordance with the present invention provide an artificial intelligence-based histopathology analysis system for breast cancer detection. The system comprises a digitization unit configured to capture and digitize biopsy slides into high-resolution digital images using Whole Slide Imaging (WSI). The system further comprises a processing unit, connected to the digitization unit, wherein the processing unit is configured to receive digitized biopsy slides from the digitization unit, analyze the received digitized images using a deep learning model trained on histopathological datasets to classify tissue samples into categories selected from normal regions, benign regions, malignant regions, or a combination thereof, generate heatmaps highlighting suspicious tissue regions based on the analyzed digitized images, compute a risk assessment score based on the generated heatmaps and historical patient data using an artificial intelligence model, and evaluate cancer risk based on the computed risk assessment score.
[006] Embodiments in accordance with the present invention provide a method for breast cancer detection using an artificial intelligence-based histopathology analysis system. The method comprises capturing and digitizing biopsy slides into high-resolution digital images using Whole Slide Imaging (WSI), receiving digitized biopsy slides from the digitization unit, analyzing the received digitized images using a deep learning model trained on histopathological datasets to classify tissue samples into categories selected from normal regions, benign regions, malignant regions, or a combination thereof, generating heatmaps highlighting suspicious tissue regions based on the analyzed digitized images, computing a risk assessment score based on the generated heatmaps and historical patient data using an artificial intelligence model and evaluating cancer risk based on the computed risk assessment score.
[007] Embodiments in accordance with the present invention further provide a method for AI-driven histopathological analysis and breast cancer detection. The method comprises steps of capturing biopsy slides and generating high-resolution digital images using Whole Slide Imaging (WSI), analyzing the digitized images using a deep learning model trained on histopathological datasets, classifying tissue samples into at least one category from normal, benign, malignant, or a combination thereof, generating heatmaps to highlight regions of interest, computing a risk assessment score using an artificial intelligence model, and transmitting the processed images and diagnostic insights to a remote platform for real-time pathologist review.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide an automated histopathology analysis system that reduces pathologists' workload and enhances diagnostic efficiency.
[009] Next, embodiments of the present application may provide AI-driven classification of tissue samples that minimizes human error and improves diagnostic accuracy.
[0010] Next, embodiments of the present application may provide real-time cancer risk assessment, allowing faster decision-making for oncologists and healthcare professionals.
[0011] Next, embodiments of the present application may provide integration with telepathology platforms, enabling remote access and collaboration between experts.
[0012] Next, embodiments of the present application may provide a self-improving AI model that continuously updates based on newly labeled histopathological data, increasing classification accuracy over time.
[0013] Next, embodiments of the present application may provide cost-effective and scalable AI-driven histopathology analysis, making cancer diagnostics more accessible in resource-limited settings.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1 illustrates artificial intelligence-based histopathology analysis system according to an embodiment of the present invention; and
[0018] FIG. 2 depicts a flowchart of a method for breast cancer detection using an artificial intelligence-based histopathology analysis system, according to an embodiment of the present invention.
[0019] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0020] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0021] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0022] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0023] Embodiments of the present invention may provide an artificial intelligence-based histopathology analysis system 100. The system 100 may be configured to automate breast cancer detection by digitizing biopsy slides, analyzing tissue samples using deep learning techniques, classifying abnormalities, generating heatmaps to highlight suspicious regions, computing a risk assessment score, and assisting pathologists in making accurate and timely diagnostic decisions. The system 100 may be further configured to enhance diagnostic accuracy, minimize human error, optimize pathologists’ workflow, provide real-time consultation, and enable remote collaboration through telepathology integration.
[0024] The system 100 may comprise a digitization unit 102, a processing unit 104, a deep learning model 106, and an artificial intelligence model 108.
[0025] In an embodiment of the present invention, the system 100 may include a digitization unit 102 configured to perform high-resolution scanning of biopsy slides using Whole Slide Imaging (WSI). The scanning of biopsy slides may be performed with a magnification of at least 40x to ensure clear visualization of histopathological structures. To achieve this, the digitization unit 102 may employ advanced optical systems, high-sensitivity sensors, and precision mechanical stages that enable accurate and rapid scanning of entire slides while maintaining high fidelity in image acquisition.
[0026] The digitization unit 102 of system 100 may be further configured to enhance image quality through preprocessing techniques such as contrast adjustment, noise reduction, and background artifact elimination, ensuring optimal clarity for subsequent AI-driven analysis. This preprocessing may leverage adaptive algorithms that dynamically adjust imaging parameters based on tissue composition and staining intensity, thereby optimizing digital slide quality for various diagnostic workflows.
[0027] Moreover, system 100 may support multiple staining techniques, including Hematoxylin and Eosin (H&E) staining, immunohistochemistry (IHC), and fluorescence imaging, to facilitate comprehensive tissue examination. The digitization unit 102 may incorporate automated slide handling mechanisms, barcode-based tracking, and batch processing capabilities to streamline the digitization workflow while minimizing manual intervention. Additionally, the digitization process may include real-time quality checks, such as focus validation and artifact detection such that only high-quality images are captured and stored for further analysis.
[0028] In an embodiment of the present invention, the system 100 may feature parallel scanning capabilities for allowing multiple slides to be processed simultaneously to accommodate a high-throughput pathology environment. Furthermore, system 100 may be designed for seamless integration with cloud-based storage and Artificial intelligence (AI) driven analytics platforms for remote access, collaborative review, and/or real-time decision support for pathologists.
[0029] In an embodiment of the present invention, the processing unit 104 may be configured to receive digitized biopsy slides from the digitization unit 102 and analyze the images using deep learning techniques to identify morphological patterns indicative of malignancy. The processing unit 104 may be further configured to generate heatmaps by overlaying color-coded probability distributions on the analyzed biopsy slides, highlighting regions with a high likelihood of cancerous growth. The processing unit 104 may also compute and transmit a real-time cancer risk assessment score, integrating patient-specific historical data to enhance predictive accuracy. The processing unit 104 may further support telepathology integration by securely transmitting AI-assisted diagnostic insights to medical professionals such as doctors, or remote pathologists via a cloud-based platform. The processing unit 104 may further be configured to enable real-time consultation and collaboration across multiple healthcare institutions.
[0030] According to embodiments of the present invention, the processing unit 104 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. In a preferred embodiment of the present invention, the processing unit 104 may be a Node Micro Controller Unit (MCU) Espressif 8266 (ESP8266). Embodiments of the present invention are intended to include or otherwise cover any type of the processing unit 104 including known, related art, and/or later developed technologies.
[0031] In an embodiment of the present invention, the deep learning model 106 may be configured to classify tissue samples into categories selected from normal regions, benign regions, malignant regions, or a combination thereof by analyzing cellular morphology, nuclear atypia, glandular structures, and other histopathological characteristics. The deep learning model 106 may be further configured to utilize Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), hybrid models, or a combination thereof to enhance classification accuracy. The deep learning model 106 may be trained on extensive histopathological datasets sourced from multiple healthcare institutions.
[0032] The histopathological datasets may include, but are not limited to, whole-slide images of biopsy samples, annotated pathology reports, cellular morphology data, immunohistochemical staining patterns, fluorescence imaging results, and multi-modal datasets combining genetic and molecular profiling information. These datasets may encompass a diverse range of tissue types, disease conditions, and staining techniques to enhance the model’s ability to generalize across various diagnostic scenarios. Furthermore, the histopathological datasets may further include both common and rare pathological cases to enable the deep learning model 106 to learn intricate variations in tissue architecture, cellular abnormalities, and disease progression patterns. The training process may incorporate expert-validated annotations, where experienced pathologists label regions of interest, classify tissue structures, and provide diagnostic insights, ensuring high accuracy and reliability of the model.
[0033] Additionally, the deep learning model 106 may undergo continuous learning, where new annotated cases and emerging histopathological findings are periodically incorporated to refine its diagnostic capabilities. The training may leverage data augmentation techniques such as rotation, scaling, and contrast normalization to improve robustness against variations in slide preparation and imaging conditions. The deep learning model 106 may also be configured to continuously improve classification accuracy by updating its parameters based on newly labeled histopathological data, enabling adaptive learning for enhanced performance over time.
[0034] In an embodiment of the present invention, the artificial intelligence model 108 may be configured to compute a risk assessment score based on the classified tissue samples by leveraging both histopathological features and patient-specific clinical history to provide a comprehensive evaluation of cancer progression. The artificial intelligence model 108 may be further configured to employ temporal data analysis techniques to predict potential cancer progression trends, and assess disease trajectory for enabling early detection of aggressive tumor behavior. The artificial intelligence model 108 may also integrate federated learning capabilities to allow artificial intelligence model 108 across multiple hospitals to collaboratively improve diagnostic accuracy while preserving patient privacy through decentralized training mechanisms. The artificial intelligence model 108 may further generate diagnostic confidence scores, providing pathologists with interpretability insights into the AI-driven classification process and ensuring informed clinical decision-making. Additionally, the artificial intelligence model 108 may facilitate real-time transmission of the evaluated cancer risk based on the computed risk assessment score for allowing for timely medical interventions and improved patient outcomes.
[0035] In an exemplary embodiment of the present invention, the system 100 may be configured to receive the digitized biopsy slides from the digitization unit 102 and analyze the received images using the deep learning model 106 trained on the histopathological datasets. The system 100 may classify the tissue samples into the normal regions, the benign regions, the malignant regions, or a combination thereof based on the extracted histopathological features. The system 100 may generate the heatmaps highlighting the suspicious tissue regions and may provide the visual overlays to indicate the potential malignancy. The system 100 may compute the risk assessment score using the artificial intelligence model 108 based on the heatmaps and historical patient data for facilitating predictive cancer risk evaluation. The evaluated cancer risk may be transmitted in real-time to pathologists via the cloud-based telepathology platform to enable remote consultation and expedited clinical decision-making. The system 100 may continuously improve diagnostic accuracy by updating the deep learning model 106 with newly labeled biopsy data, for adaptive learning and enhancing performance over time.
[0036] Embodiment of the present invention may provide a method 200 for breast cancer detection using the artificial intelligence-based histopathology analysis system 100.
[0037] At step 202, the system 100 may capture and digitize the biopsy slides into the high-resolution digital images using the Whole Slide Imaging (WSI) using the digitization unit 102.
[0038] At step 204, the system 100 may receive the digitized biopsy slides from the digitization unit 102 using the processing unit 104.
[0039] At step 206, the system 100 may analyze the received digitized images using the deep learning model 106 trained on the histopathological datasets to classify the tissue samples into the categories such as the normal regions, the benign regions, the malignant regions, and so forth.
[0040] At step 208, the system 100 may generate the heatmaps highlighting the suspicious tissue regions based on the analyzed digitized images.
[0041] At step 210, the system 100 may compute the risk assessment score based on the generated heatmaps and the historical patient data using the Artificial intelligence model 108.
[0042] At step 212, the system 100 may evaluate the cancer risk based on the computed risk assessment score.
[0043] At step 214, the system 100 may transmit the evaluated cancer risk based on the computed risk assessment score for the real-time consultation to the medical professional.
[0044] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0045] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. An artificial intelligence-based histopathology analysis system (100) for breast cancer detection, comprising:
a digitization unit (102) configured to capture and digitize biopsy slides into high-resolution digital images using Whole Slide Imaging (WSI);
a processing unit (104), connected to the digitization unit, characterized in that the processing unit (104) is configured to:
receive the digitized biopsy slides from the digitization unit (102);
analyze the received digitized images using a deep learning model (106) trained on histopathological datasets to classify tissue samples into categories selected from normal regions, benign regions, malignant regions, or a combination thereof;
generate heatmaps highlighting suspicious tissue regions based on the analyzed digitized images;
compute a risk assessment score based on the generated heatmaps and historical patient data using an Artificial intelligence model (108); and
evaluate cancer risk based on the computed risk assessment score.
2. The system (100) as claimed in claim 1, wherein the digitization unit (102) is configured to perform a high-resolution scanning using the Whole Slide Imaging (WSI) with at least 40x magnification.
3. The system (100) as claimed in claim 1, wherein the deep learning model (106) is selected from a convolutional neural network (CNN), a vision transformer (ViT), a hybrid model trained on histopathology datasets, or a combination thereof.
4. The system (100) as claimed in claim 1, wherein the processing unit (104) is configured to overlay colored intensity regions on the digitized images to indicate areas of high malignancy probability.
5. The system (100) as claimed in claim 1, wherein the artificial intelligence model (108) is configured to correlate the classified tissue samples with the historical patient data to predict potential cancer progression trends using a temporal data analysis.
6. The system (100) as claimed in claim 1, the processing unit (104) is configured to transmit the evaluated cancer risk based on the computed risk assessment score for real-time consultation to a medical professional.
7. The system (100) as claimed in claim 1, the processing unit (104) is configured to continuously improve classification accuracy by updating the deep learning model (106) based on newly labeled histopathological data.
8. A method (200) for breast cancer detection using an artificial intelligence-based histopathology analysis system (100), comprising steps of:

capturing and digitizing biopsy slides into high-resolution digital images using Whole Slide Imaging (WSI);
receiving the digitized biopsy slides from the digitization unit (102);
analyzing the received digitized images using a deep learning model (106) trained on histopathological datasets to classify tissue samples into categories selected from normal regions, benign regions, malignant regions, or a combination thereof;
generating heatmaps highlighting suspicious tissue regions based on the analyzed digitized images;
computing a risk assessment score based on the generated heatmaps and historical patient data using an Artificial intelligence model (108); and
evaluating cancer risk based on the computed risk assessment score.
9. The method (200) as claimed in claim 8, wherein the deep learning model (106) is selected from a convolutional neural network (CNN), a vision transformer (ViT), a hybrid model trained on histopathology datasets, or a combination thereof.
10. The method (200) as claimed in claim 8, comprising a step of transmitting the evaluated cancer risk based on the computed risk assessment score for real-time consultation.

Date: March 06, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant