Anomaly detection Using Generative Adversarial Networks

Anomaly detection Using Generative Adversarial Networks

Obtaining models that capture anomalies relevant for disease progression and treatment monitoring .

Artificial Intelligence

Description

Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection.Performing unsupervised learning to identify anomalies in imaging data as candidates for markers. By using deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, we can achieve high accuracy in anomaly detection. Medical imaging enables the observation of markers correlating with disease status, and treatment response. Generative model will generate anomalies . The training procedure for Generative model is to maximize the probability of discriminative model making a mistake.

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Github repository

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Prajjwal B. added photos to project Anomaly detection Using Generative Adversarial Networks

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Anomaly detection Using Generative Adversarial Networks

Models are typically based on large amounts of data with annotated examples of
known markers aiming at automating detection.Performing unsupervised learning to
identify anomalies in imaging data as candidates for markers. By using deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, we can achieve high accuracy in anomaly detection. Medical imaging enables the observation of markers correlating with disease status, and treatment response. Generative model will generate anomalies . The training procedure for Generative model is to maximize the probability of discriminative model making a mistake.

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Prajjwal B. created project Anomaly detection Using Generative Adversarial Networks

Medium 0f92f64e ac7f 468f 9acf c9392c6ff6df

Anomaly detection Using Generative Adversarial Networks

Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection.Performing unsupervised learning to identify anomalies in imaging data as candidates for markers. By using deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, we can achieve high accuracy in anomaly detection. Medical imaging enables the observation of markers correlating with disease status, and treatment response. Generative model will generate anomalies . The training procedure for Generative model is to maximize the probability of discriminative model making a mistake.

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