EXPERT PANEL MODELS FOR NEURAL NETWORK ANOMALY DETECTION AND THWARTING ADVERSARIAL ATTACKS

    公开(公告)号:US20250165595A1

    公开(公告)日:2025-05-22

    申请号:US18951860

    申请日:2024-11-19

    Applicant: Alcon, Inc.

    Abstract: A system includes a machine learning (ML) engine in which a data store is coupled to a processing system. The processing system executes code to receive a dataset from the data store, and produces three models each comprising three tests. Each of the tests seeks to detect one of three anomaly types corresponding to each of the models. The processing system performs at least two of the three tests relating to each of the three anomaly types. Separately for each anomaly type, the processing system detects an anomaly when two-out-of-three (2oo3) tests conclude that the anomaly is present in the dataset. The dataset including the flagged anomalies is stored in a data repository. The anomaly is filtered from the dataset. The processing system is configured to use data from the dataset to retrain an existing trained ML model.

    INTEGRATED ANALYSIS OF MULTIPLE SPECTRAL INFORMATION FOR OPHTHALMOLOGY APPLICATIONS

    公开(公告)号:US20240032784A1

    公开(公告)日:2024-02-01

    申请号:US18358881

    申请日:2023-07-25

    Applicant: Alcon Inc.

    CPC classification number: A61B3/0025 A61B3/14 G16H50/30

    Abstract: In certain embodiments, an ophthalmic system and computer-implemented method for analyzing multiple spectral information to generate ophthalmic information are described. In an exemplary ophthalmic system, multiple spectral information associated with an eye of a patient is captured via an imaging system. A first set of information and a second set of information are extracted from the multiple spectral information. A visualization of the multiple spectral information is generated using the first set of information. The first set of information and the second set of information are evaluated using different deep learning models to generate ophthalmic information. The ophthalmic information is sent to a user for diagnostic evaluation.

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