Search Results for author: Akshay S. Chaudhari

Found 15 papers, 7 papers with code

Enhance the Image: Super Resolution using Artificial Intelligence in MRI

no code implementations19 Jun 2024 Ziyu Li, Zihan Li, Haoxiang Li, Qiuyun Fan, Karla L. Miller, Wenchuan Wu, Akshay S. Chaudhari, Qiyuan Tian

This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations.

Deep Learning Image Super-Resolution

CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation

1 code implementation22 Jan 2024 Zhihong Chen, Maya Varma, Jean-Benoit Delbrouck, Magdalini Paschali, Louis Blankemeier, Dave Van Veen, Jeya Maria Jose Valanarasu, Alaa Youssef, Joseph Paul Cohen, Eduardo Pontes Reis, Emily B. Tsai, Andrew Johnston, Cameron Olsen, Tanishq Mathew Abraham, Sergios Gatidis, Akshay S. Chaudhari, Curtis Langlotz

However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation.

Benchmarking Fairness +2

Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning

no code implementations14 Oct 2022 Jeffrey Dominic, Nandita Bhaskhar, Arjun D. Desai, Andrew Schmidt, Elka Rubin, Beliz Gunel, Garry E. Gold, Brian A. Hargreaves, Leon Lenchik, Robert Boutin, Akshay S. Chaudhari

Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medical imaging field.

Image Segmentation Segmentation +2

Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction

1 code implementation21 Apr 2022 Beliz Gunel, Arda Sahiner, Arjun D. Desai, Akshay S. Chaudhari, Shreyas Vasanawala, Mert Pilanci, John Pauly

Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.

MRI Reconstruction

Measuring Robustness in Deep Learning Based Compressive Sensing

1 code implementation11 Feb 2021 Mohammad Zalbagi Darestani, Akshay S. Chaudhari, Reinhard Heckel

In order to understand the sensitivity to such perturbations, in this work, we measure the robustness of different approaches for image reconstruction including trained and un-trained neural networks as well as traditional sparsity-based methods.

Compressive Sensing Deep Learning +1

Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks

no code implementations5 Feb 2019 Arjun D. Desai, Garry E. Gold, Brian A. Hargreaves, Akshay S. Chaudhari

High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications.

Deep Learning Segmentation +1

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