Berk Tinaz

Berk Tinaz

University of Southern California

H-index: 5

North America-United States

About Berk Tinaz

Berk Tinaz, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at University of Southern California, specializes in the field of Machine Learning, Deep Learning, Computer Vision.

His recent articles reflect a diverse array of research interests and contributions to the field:

Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models

Diracdiffusion: Denoising and incremental reconstruction with assured data-consistency

Humus-net: Hybrid unrolled multi-scale network architecture for accelerated mri reconstruction

Semi-supervised learning of MRI synthesis without fully-sampled ground truths

Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery

A semi-supervised learning framework for jointly accelerated multi-contrast MRI synthesis without fully-sampled ground-truths

Semi-supervised learning of mutually accelerated MRI synthesis without fully-sampled ground truths

Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks

Berk Tinaz Information

University

Position

___

Citations(all)

197

Citations(since 2020)

197

Cited By

65

hIndex(all)

5

hIndex(since 2020)

5

i10Index(all)

5

i10Index(since 2020)

5

Email

University Profile Page

Google Scholar

Berk Tinaz Skills & Research Interests

Machine Learning

Deep Learning

Computer Vision

Top articles of Berk Tinaz

Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models

arXiv preprint arXiv:2309.06642

2023/9/12

Diracdiffusion: Denoising and incremental reconstruction with assured data-consistency

arXiv preprint arXiv:2303.14353

2023/3/25

Humus-net: Hybrid unrolled multi-scale network architecture for accelerated mri reconstruction

Advances in Neural Information Processing Systems

2022/12/6

Semi-supervised learning of MRI synthesis without fully-sampled ground truths

IEEE Transactions on Medical Imaging

2022/8/16

Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery

Medical Image Analysis

2022/5/1

A semi-supervised learning framework for jointly accelerated multi-contrast MRI synthesis without fully-sampled ground-truths

29th annual meeting of International Society for Magnetic Resonance Imaging (ISMRM),(Virtual Conference)

2021

Semi-supervised learning of mutually accelerated MRI synthesis without fully-sampled ground truths

arXiv preprint arXiv:2011.14347

2020/11/29

Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks

IEEE Journal of Selected Topics in Signal Processing

2020/6/11

See List of Professors in Berk Tinaz University(University of Southern California)

Co-Authors

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