Activity Feed

Medium 15267900 369724496694704 7602675094723525974 n

Prajjwal B. posted an update in Student Developers for AI

Medium 15267900 369724496694704 7602675094723525974 n

Prajjwal Bhargava

Hello everyone! I am a sophomore pursuing my bachelor's in computer science. I am deeply interested in machine learning, deep learning, computer vision, machine perception. My projects can found on https://github.com/prajjwal1 . If you would like to contribute to my projects (open source ) , feel free to send pull requests with well explanatory commits. Occasionally I write on my blog https://prajjwal1.github.io . I am currently working on Generative adversarial networks and recurrent neural networks. I would like to contribute on projects which are good enough for publication (particularly on arXiv) .

Find me on https://twitter.com/prajjwal_1

15267900 369724496694704 7602675094723525974 n

Prajjwal B. added photos to 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.

Medium 15267900 369724496694704 7602675094723525974 n

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.

See More

About

Featured Projects

See All

No users to show at the moment.

No projects to show at the moment.

No topics to show at the moment.

Bigger  dsc0075 copy00
  • Projects 0
  • Followers 4

Dhruv Patel

Chennai, Tamil Nadu, India