公开(公告)号：US20240394840A1

公开(公告)日：2024-11-28

申请号：US18669939

申请日：2024-05-21

Applicant: Google LLC

Inventor： Elchonon Zeav Lapin ,  Xibing Yang ,  Amit Handa ,  Apurv Suman ,  Siddhant Mittal ,  Ashish Dilipchand Bora ,  Thorne Wolfenbarger ,  Naga Sreenivas Meruva ,  Yudong Sun ,  Rahul Guin ,  Arie Sharon ,  Beatriz Alessio Robles Orozco ,  Yuanzhen Li ,  Zhongyue Zheng ,  Mohammad Izadi

IPC: G06T5/50 ,  G06T3/40 ,  G06T5/70 ,  G06T7/13 ,  G06T7/194 ,  G06T7/50 ,  H04N5/272

Abstract: Using artificial intelligence (AI), imagery may be created for content in response to verbal or textual input. The imagery includes an object, such as a product, and a quality of the image is improved using pre-processing techniques before the image is generated and post-processing techniques after the image is generated. The pre-processing may include upscaling the object in the original image, segmenting the object from its background in the captured image, adding an outline or border stroke to the object. The post-processing techniques may include removing the object from the AI-generated background while keeping shadows and other effects in place, blurring portions of the AI-generated background where the object will be positioned, removing the outline from the object, and re-positioning the object in the AI-generated background with the outline removed.

公开(公告)号：US20220384042A1

公开(公告)日：2022-12-01

申请号：US17775139

申请日：2020-10-13

Applicant: Google LLC

Inventor： Andrew Beckmann Sellergren ,  Shravya Ramash Shetty ,  Siddhant Mittal ,  David Francis Steiner ,  Anna Majkowska ,  Gavin Elliott Duggan

IPC: G16H50/20 ,  G16H30/20

Abstract: The present disclosure provides systems and methods for training and/or employing machine-learned models (e.g., artificial neural networks) to diagnose chest conditions such as, as examples, pneumothorax, opacity, nodules or masses, and/or fractures based on chest radiographs. For example, one or more machine-learned models can receive and process a chest radiograph to generate an output. The output can indicate, for each of one or more chest conditions, whether the chest radiograph depicts the chest conditions (e.g., with some measure of confidence). The output of the machine-learned models can be provided to a medical professional and/or patient for use in providing treatment to the patient (e.g., to treat a detected condition).