3 results about "Mammographic image" patented technology

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.

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.