公开(公告)号：US20230260652A1

公开(公告)日：2023-08-17

申请号：US18012187

申请日：2021-12-10

Applicant: Google LLC

Inventor： Shekoofeh Azizi ,  Wen Yau Aaron Loh ,  Zachary William Beaver ,  Ting Chen ,  Jonathan Paul Deaton ,  Jan Freyberg ,  Alan Prasana Karthikesalingam ,  Simon Kornblith ,  Basil Mustafa ,  Mohammad Norouzi ,  Vivek Natarajan ,  Fiona Keleher Ryan

IPC: G16H50/20 ,  G06T7/00 ,  G06V10/74 ,  G16H30/40 ,  G16H50/70

CPC classification number: G16H50/20 ,  G06T7/0012 ,  G06V10/761 ,  G16H30/40 ,  G16H50/70 ,  G06T2207/20081 ,  G06T2207/20132

Abstract: Systems and methods can perform self-supervised machine learning for improved medical image analysis. As one example, self-supervised learning on ImageNet, followed by additional self-supervised learning on unlabeled medical images from the target domain of interest, followed by fine-tuning on labeled medical images from the target domain significantly improves the accuracy of medical image classifiers such as, for example diagnostic models. Another example aspect of the present disclosure is directed to a novel Multi-Instance Contrastive Learning (MICLe) method that uses multiple different medical images that share one or more attributes (e.g., multiple images that depict the same underlying pathology and/or the same patient) to construct more informative positive pairs for self-supervised learning.

公开(公告)号：US20240428937A1

公开(公告)日：2024-12-26

申请号：US18338003

申请日：2023-06-20

Applicant: Google LLC

Inventor： Vivek Natarajan ,  Karan Singhal ,  Shekoofeh Azizi ,  Alan Prasana Karthikesalingam ,  Tao Tu ,  Seyedeh Sara Mahdavi ,  Christopher Semturs

IPC: G16H50/20

Abstract: An aspect of the present disclosure provides an example method comprising: receiving an input query associated with a particular task domain of a plurality of available task domains; obtaining a machine-learned prompt component and a curated prompt component, wherein the machine-learned prompt component comprises a plurality of machine-learned prompt values for the plurality of available task domains, and wherein the curated prompt component comprises a plurality of exemplar prompt values corresponding to one or more embedded natural language exemplars for the particular task domain from domain experts; and generating an output responsive to the input query by processing a combined prompt and the input query using a pre-trained machine-learned model, wherein the combined prompt comprises the machine-learned prompt component and the curated prompt component.