13 results about "Mammographic image" patented technology

A system for improved breast cancer prognosis using multimodal machine learning; the system includes: a central processing unit (CPU) configured to coordinate and execute data processing tasks; a multimodal data acquisition interface for communication with multiple diagnostic devices and hospital databases, wherein the multimodal data acquisition interface is configured to receive different datasets, including mammography images, histopathological images, genetic profiles and medical records, a feature extraction unit that is operationally connected to the central processing unit, wherein the feature extraction unit is configured to apply deep learning models to extract relevant features from the received datasets, including cell structures from histopathological images, masses or calcifications from mammographic images, and genetic mutations from genetic profiles; a machine learning engine integrated into the central processing unit and the feature extraction unit, wherein the machine learning engine is configured to analyze the extracted features using multimodal learning techniques, and wherein the machine learning engine is further configured to combine data from multiple modalities to generate a prognosis for breast cancer, the prognosis comprising an assessment of the disease course, the risk of relapse, and the likely treatment response; a forecast reporting unit in communication with the machine learning machine, wherein the forecast reporting unit is configured to generate a comprehensive forecast report in both visual and text formats, the report presenting multimodal analysis results in a format interpretable by medical professionals; and a power supply configured to continuously power the system, the power supply also including an uninterruptible power supply (UPS) to ensure uninterrupted operation of the system in the event of power outages or fluctuations.

Training procedure for artificial intelligence for mammographic data analysis for breast cancer detection including acquiring mammographic data from a plurality of sources and including mammographic images, report texts relating to images, and structured data obtained from SIO, EMR, BI-RADS and MOM including at least metadata relating to part of the images, processing mammographic data through algorithms implementing a multimodal deep neural network (DNN) developing a mammographic data analysis model by performing learning based on sub-phases of first multi-label classification of each image implemented through a model with Encoder-Decoder architecture based on convolutional neural network (CNN) and / or Transformers, association of parts of report texts with images and / or parts of structured data, implemented through a model with Encoder-Decoder architecture based on a bidirectional long-term memory (Bi-LSTM) and / or Transformers, second multi-label classification of mammographic structured data implemented through a model with Encoder-Decoder architecture based on CNN and / or Transformers.

Described herein are systems, methods, and instrumentalities associated with processing mammogram images using machine learning based techniques. An apparatus as described herein may obtain a mammographic image of a person, extract features from the mammographic image using a feature encoder, and predict a health condition of the person based on the extracted features. The feature encoder may be trained using a self-supervised technique and based on multi-view mammogram images that may belong to a same person or to different people.

Described herein are systems, methods, and instrumentalities associated with processing mammogram images using machine learning based techniques. An apparatus as described herein may obtain a mammographic image of a person, extract features from the mammographic image using a feature encoder, and predict a health condition of the person based on the extracted features. The feature encoder may be trained using a self-supervised technique and based on multi-view mammogram images that may belong to a same person or to different people.

A computer-implemented method for generating synthetic mammographic images using a modified diffusion model has as novelty three training phases in which modified diffusion is performed, namely: in a second stage 200 of local context generations, over an image representing a third channel, in a step 208 of adding noise to the image patch, and in a step 209 of training the neural network to remove the noise, and also modified diffusion is performed in a third phase 300 of generating full-resolution patches, in a step 308 of denoising the image patch, and in a step 310 of training a neural network to remove noise. Also, the method includes working with patches in the image in the second phase 200 in steps 204-210 and in a third phase 300 in steps 304-311. The method includes training in phase 100 on an image representing a single channel, a global context, with steps 101-106, then training over the images representing the first, second, and third channel: in phase 200 with steps 201-210 and in phase 300 with steps 301-311 and finally phase 400 of generating a full-resolution mammogram image. Phase 400 includes: subphase 401 where noise is programmatically generated and in step 403 the neural network removes it and provides an output image at a resolution of 256x256; subphase 405 with step 406 where the input image from the first phase is loaded and steps 407 are then performed-411; step 412 of integrating patches with local context in a way that ensures smooth transitions during integration, which is an innovative step; then subphase 413 where, in step 414, the medium-resolution image obtained by integrating the patches from the second phase 200 is loaded, after which steps 415-420 are performed. then step 421 of integrating the patches in the image follows, and finally, the full-resolution output image is obtained in step 422. The patch integration in step 412 is innovative and ensures smooth transitions in the image.

The invention relates to an X-ray mammography apparatus (100, 200). According to the invention, the apparatus comprises: a first source (10a) emitting a first X-ray beam (11a); a second source (10b) emitting a second X-ray beam (11b); an X-ray image detector (30) arranged in a first imaging configuration to capture the first X-ray beam (11a) transmitted through a portion of the breast, and arranged in a second imaging configuration to capture the second transmitted X-ray beam (11b), the apparatus (100, 200) having, in the second imaging configuration, an optical magnification greater than or equal to 8 and less than 20; and a processing unit (40) configured to determine a mammographic image by absorption from the first imaging configuration and to reconstruct a phase-contrast image from the second imaging configuration.

A mammographic imaging device for analysis and detection of possible inhomogeneities in breast tissue of a patient using laser light in the near-infrared in diffuse reflectance geometry, includes a patient horizontal support comprising at least one transparent window in its cross-section and a measuring mechanism below the at least one transparent window and carried by said support. The measuring mechanism includes a laser-light producing mechanism for producing laser light beams in the near-infrared, a directing mechanism for directing said laser light beams towards the at least one transparent window, at least one wavelength filter, a light-sensing and imaging mechanism for sensing light and producing images and a controlling, processing and image-normalizing unit for controlling, processing and normalizing images.

Techniques for patient positioning quality assurance prior to mammographic image acquisition are described. A system may include an x-ray source configured to expose human tissue to radiation. An x-ray detector module may be configured to detect the radiation and generate a pre-diagnostic image of the human tissue. A position analysis module may be configured to receive the pre-diagnostic image and determine whether the human tissue is positioned correctly based upon one or more predefined positioning criteria. A non-transitory computer-readable storage medium may be configured to store the results of the positional analysis module determination. A display may be configured to display the determination of the position analysis module within a user interface. Other embodiments are described and claimed.

Techniques for patient positioning quality assurance prior to mammographic image acquisition are described. A system may include an x-ray source configured to expose human tissue to radiation. An x-ray detector module may be configured to detect the radiation and generate a pre-diagnostic image of the human tissue. A position analysis module may be configured to receive the pre-diagnostic image and determine whether the human tissue is positioned correctly based upon one or more predefined positioning criteria. A non-transitory computer-readable storage medium may be configured to store the results of the positional analysis module determination. A display may be configured to display the determination of the position analysis module within a user interface. Other embodiments are described and claimed.

The present application provides a method, apparatus, and computer-readable storage medium for processing mammographic images. The method comprises: encoding an image to be processed using an encoder to obtain encoding information corresponding to the image to be processed; decoding the encoding information corresponding to the image to be processed using a decoder to obtain calcification point segmentation information corresponding to the image to be processed; extracting features from the image to be processed using the first layer of the encoder or any layer of the encoder or decoder from the first layer to the next layer to obtain a feature map corresponding to the image to be processed; and inputting the feature map corresponding to the image to be processed into a fully connected layer to obtain benign and malignant classification information of the calcification clusters corresponding to the image to be processed. The present application can reduce the time and effort doctors spend on visual marking, improve the efficiency of mammographic image processing, and has a wide range of applications.

Systems and methods are provided for generating training data for training a neural network. The neural network may be a machine learning model. The neural network may be a U-Net neural network. Training data for training a neural network may be generated by combining X-ray image data and non-X-ray image data. Training data for training a neural network may be generated by superimposing data having a characteristic related to a lesion onto X-ray image data. The superimposed data may be used to simulate calcification features in mammographic images. Processing circuitry is configured to perform noise reduction by inputting an input image of a breast into a trained machine-learning model to output an output image in which noise included in the input image is reduced in the output image while leaving a characteristic of a lesion included in the input image undeleted in the output image.

Systems, apparatus, articles of manufacture, and methods are disclosed that to configure a first medical imaging device, capture a first mammographic image of a patient using the first medical imaging device, analyze the first medical image, determine, in response to an analysis of the first medical image, to capture a second medical image, configure at least one of the first medical imaging device or a second medical imaging device for the second medical image different from the first medical image, capture the second medical image, and transmit the first medical image and the second medical image to an external device for processing and generating a next action with respect to the patient.

Examples of the present disclosure describe systems and methods for using machine learning (ML) to predict optimal exposure technique for acquiring mammographic images. In aspects, training data may be collected from one or more data sources. The training data may comprise sample data of patient attribute data and sample image attribute data, image metadata, image pixel data, and / or exposure technique parameters. The training data may be used to train an ML model to determine the optimal exposure technique parameters for acquiring mammographic images of a patient. After the ML model has been trained, patient data that is collected from a patient during a patient visit may be provided as input to the ML model. The ML model may output optimal exposure technique parameters for the patient in real-time. The real-time output of the ML model may be used to generate one or more mammographic images of the patient.