Description:FIELD OF THE INVENTION
The invention relates to medical imaging, specifically to deep learning frameworks for three-dimensional medical image segmentation and tumor analysis. It focuses on transformer-driven architectures that integrate global spatial context and multimodal fusion to improve diagnostic precision in oncology.
BACKGROUND OF THE INVENTION
Early and accurate tumor detection through 3D medical imaging remains a crucial challenge in oncology. While Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) offer volumetric data for detailed visualization, their interpretation relies heavily on radiologists’ expertise. Manual examination is not only time-consuming and subjective but also prone to inter-observer variability.
Traditional machine learning and CNN-based solutions for tumor detection have improved consistency but are limited by:
• Poor scalability with large 3D datasets.
• Inability to capture long-range dependencies and spatial context in volumetric scans.
• Lack of real-time inference and generalization across tumor types and imaging modalities.
As a result, there exists a significant unmet need for intelligent, transformer-based deep learning systems that can:
• Efficiently analyze large-scale 3D medical imaging datasets.
• Capture multi-scale and spatial context.
• Deliver high diagnostic accuracy with minimal human intervention.
US20230410483: A method includes obtaining a first training data set including unannotated multi-dimensional medical images and executing a self-supervised masked image modeling (MIM) training process to pre-train an image encoder on the first training data set. The method also includes obtaining a second training data set that includes annotated multi-dimensional medical images. Here, each annotated multi-dimensional medical image includes a plurality of image voxels each paired with a corresponding ground-truth label indicating a class the corresponding image voxel belongs to. The method also includes executing a supervised training process to train an image analysis model on the second training data set to teach the image analysis model to learn how to predict the corresponding ground-truth labels for the plurality of image voxels of each annotated multi-dimensional medical image. The image analysis model incorporates the pre-trained image encoder.
US20240362788: A system for 3D medical image segmentation includes a medical imaging device for obtaining a plurality of 2D images forming a volumetric image, processing circuitry, and a display. The processing circuitry is configured with a first stage to divide the volumetric image into 3D image patches, a hierarchical encoder-decoder structure in which resolution of features of the 3D image patches is decreased by a factor of two in each of a plurality of stages of the encoder, an encoder output connected to the decoder via skip connections, and a convolutional block to produce a voxel-wise final segmentation mask. The encoder includes a plurality of efficient paired attention blocks each with a spatial attention branch and a channel attention branch that learn respective spatial and channel attention feature maps. The display displays the final segmentation mask.
Tumor detection and analysis using volumetric scans such as MRI, CT, and PET is a complex process that demands expert interpretation. Manual examination is time-consuming, subjective, and prone to inter-observer variability. Existing CNN-based approaches, while helpful, fail to capture long-range dependencies in volumetric data, lack multimodal integration, and often require excessive computation. Moreover, current systems do not adequately provide real-time inference or explainability, making them less reliable for clinical decision-making. The present invention solves these challenges by introducing a transformer-based system that learns spatial and inter-slice context, integrates multimodal information, operates efficiently on large-scale data, and delivers interpretable results with confidence heatmaps.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention introduces a transformer-driven deep learning system for analyzing three-dimensional medical images and performing tumor segmentation and classification. It employs 3D Vision Transformer encoders to model spatial dependencies across volumetric slices and tumor boundaries, combined with a CNN-based decoder to preserve structural resolution during segmentation.
The system accepts volumetric imaging data, preprocesses and normalizes it, and then applies patch-wise embeddings to generate transformer inputs. Attention mechanisms dynamically identify tumor regions, while feature fusion enables cross-modality learning, for example, MRI combined with PET scans. The architecture supports classification of tumor state, such as benign, malignant, or metastatic, while producing segmentation maps and attention heatmaps for interpretability.
Deployment is optimized for GPU, TPU, or cloud platforms, with integration into PACS infrastructure and teleradiology workflows. This ensures scalability, clinical usability, and reduced computational burden compared to conventional volumetric CNN systems. The invention thus delivers fast, accurate, and interpretable tumor analysis for real-world oncology applications.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention introduces a transformer-driven deep learning framework that enhances 3D medical image analysis and tumor classification through:
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The invention introduces a transformer-driven deep learning framework that enhances 3D medical image analysis and tumor classification through:
(i) Model Architecture:
• Utilizes 3D Vision Transformers (ViT3D) with self-attention to learn spatial relationships across slices and tumor boundaries.
• Combines a hierarchical transformer encoder with a CNN-based decoder to preserve spatial resolution during segmentation.
(ii) Data Pipeline:
• Volumetric scans (MRI, CT, PET) are preprocessed and normalized across all axes.
• Patch-wise embedding is performed for each 3D sub-volume.
• Attention masks learn tumor regions dynamically, reducing false positives.
(iii) Tumor Detection and Segmentation:
• Multi-head attention modules extract long-range inter-slice context.
• Feature fusion enables cross-modality learning (MRI+PET fusion for tumor type classification).
• Output is a binary/ternary classification: benign, malignant, or metastasized tumor state.
(iv) Deployment and Interface:
• Lightweight deployment on GPU/TPU for real-time radiology assistance.
• Visual overlays highlight tumor boundaries and confidence heatmaps.
• Web interface supports PACS integration and cloud upload for teleradiology.
This system uniquely applies transformer-based attention mechanisms to full 3D medical scans, enabling:
• Global context awareness across volumetric slices.
• Efficient multi-modal fusion of imaging data.
• Real-time, high-accuracy classification with reduced computational overhead.
No current commercial product fully integrates transformer-based modeling, multimodal image fusion, and clinical deployment tools into a unified, lightweight architecture.
DETAILED DESCRIPTION
The system begins with an input preprocessing pipeline designed to handle volumetric imaging data from MRI, CT, and PET modalities. The data is normalized across axes to standardize contrast and intensity variations. Following this step, patch-wise embeddings are generated, converting three-dimensional volumes into a form suitable for transformer-based processing. These embedding captures local voxel-level detail while preparing the data for global attention modeling.
A hierarchical transformer encoder processes the embedded patches. Self-attention mechanisms within the encoder capture relationships across slices and between regions separated in space, ensuring that tumor morphology and contextual features are preserved. Multi-head attention enables the model to recognize long-range dependencies while remaining computationally efficient.
To preserve spatial resolution during segmentation, the transformer encoder is coupled with a convolutional decoder that reconstructs the three-dimensional segmentation map. This hybrid approach combines the strengths of attention-based global modeling with convolution-based local detail preservation.
Within the tumor detection process, attention masks dynamically highlight regions of interest where tumor presence is most likely. These masks reduce false positives and help in distinguishing between background tissue and diagnostically relevant structures.
The model further incorporates multimodal feature fusion, enabling integration of MRI, CT, and PET scans. Cross-attention mechanisms align complementary features across modalities, improving accuracy in distinguishing tumor types and states. For example, PET data can highlight metabolic activity, while MRI provides structural details, and their fusion enhances diagnostic clarity.
The classification module utilizes the extracted features to assign tumor state labels such as benign, malignant, or metastasized. This dual capability of segmentation and classification allows the system to serve as a comprehensive diagnostic support tool.
The output of the system includes segmentation overlays superimposed on the original scans, confidence heatmaps highlighting attention regions, and classification results. The interpretability provided by attention visualization strengthens clinical trust and aids radiologists in decision-making.
Deployment considerations have been built into the invention. The architecture is optimized for lightweight inference, enabling use on GPU clusters, TPUs, or cloud platforms. The system supports integration into hospital PACS environments, allowing seamless workflow adoption.
The invention improves upon existing CNN-based volumetric models by reducing computational load while maintaining high segmentation accuracy. Unlike traditional CNNs that process limited receptive fields, the transformer-driven approach captures both local and global context, which is critical for volumetric tumor analysis.
This architecture ensures generalization across tumor types and imaging modalities. The modular design allows retraining or fine-tuning for new datasets without redesigning the full model. This adaptability extends its applicability to diverse clinical environments.
The system also addresses scalability by using patch-based embeddings that reduce memory overhead during training and inference. Hospitals with limited computational infrastructure can still deploy the invention without compromising performance.
The invention provides explainability through attention heatmaps that reveal which regions influenced the model’s decision. This feature enhances transparency and improves acceptance among clinicians, reducing the perception of the system as a “black box.”
Robustness against imaging artifacts and inter-slice inconsistencies is achieved through multimodal fusion and attention-driven learning, which together mitigate common errors in automated tumor detection.
The system is suitable for real-time applications in oncology, enabling faster turnaround of diagnostic reports and reducing radiologist workload. It can also support remote consultations through cloud-based deployment, improving accessibility in under-resourced regions.
In summary, the invention integrates hierarchical transformer encoders, convolutional decoders, multimodal fusion, and attention-based explainability into a unified system that advances the state of the art in three-dimensional tumor analysis.
BEST METHOD OF WORKING
The best method of working involves implementing the system in a clinical radiology environment where volumetric imaging modalities such as MRI and PET are commonly used. The scans are first preprocessed through normalization and patch embedding, then passed through the hierarchical transformer encoder to capture spatial and inter-slice context. The convolutional decoder reconstructs the segmentation maps, while multimodal fusion integrates complementary information across imaging modalities. The system generates both tumor classification results and segmentation overlays, with attention heatmaps for interpretability. The deployment is best realized on GPU or TPU infrastructure integrated into PACS, enabling real-time tumor detection and teleradiology support.
 
, Claims:1. A medical image analysis system for three-dimensional tumor segmentation and classification comprising: an input preprocessing module configured to normalize volumetric scans and generate patch-wise embeddings; a hierarchical transformer encoder configured to capture long-range spatial dependencies and inter-slice context;
a convolutional decoder coupled to the encoder to preserve spatial resolution during segmentation; one or more attention masks configured to dynamically identify tumor regions; a multimodal fusion module configured to integrate features from different imaging modalities; a classification unit configured to determine tumor state as benign, malignant, or metastasized; and an output interface configured to generate segmentation overlays and confidence heatmaps for diagnostic support.
2. The system as claimed in claim 1, wherein the transformer encoder comprises a three-dimensional vision transformer with multi-head attention to capture volumetric spatial relationships.
3. The system as claimed in claim 1, wherein the multimodal fusion module integrates MRI, CT, and PET features using cross-attention mechanisms.
4. The system as claimed in claim 1, wherein the attention masks reduce false positives by dynamically focusing on regions of diagnostic relevance.
5. The system as claimed in claim 1, wherein the convolutional decoder reconstructs fine spatial details to preserve tumor boundaries.
6. The system as claimed in claim 1, wherein the output interface provides interpretability through heatmaps indicating model attention regions.
7. The system as claimed in claim 1, wherein the system is optimized for real-time inference on GPU, TPU, or cloud platforms.
8. The system as claimed in claim 1, wherein the system is integrated with hospital PACS infrastructure for clinical deployment.
9. A method for analyzing three-dimensional medical images for tumor segmentation and classification comprising: receiving volumetric medical scan data; preprocessing the data through normalization and patch embedding; extracting features using a hierarchical transformer encoder; decoding segmentation maps with a convolutional decoder;
applying multimodal fusion to combine features from different imaging modalities; classifying tumor state as benign, malignant, or metastasized; and
generating segmentation overlays and attention heatmaps as outputs.
10. The method as claimed in claim 9, wherein the multimodal fusion enables integration of MRI, CT, and PET scans for improved diagnostic accuracy.