Search Results for author: Akshay Chaudhari

Found 16 papers, 8 papers with code

ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data

1 code implementation ICCV 2023 Maya Varma, Jean-Benoit Delbrouck, Sarah Hooper, Akshay Chaudhari, Curtis Langlotz

The first key contribution of this work is to demonstrate through systematic evaluations that as the pairwise complexity of the training dataset increases, standard VLMs struggle to learn region-attribute relationships, exhibiting performance degradations of up to 37% on retrieval tasks.

object-detection Representation Learning +2

Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays

1 code implementation30 Jan 2023 Rogier van der Sluijs, Nandita Bhaskhar, Daniel Rubin, Curtis Langlotz, Akshay Chaudhari

Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent.

Anomaly Detection Representation Learning +1

RoentGen: Vision-Language Foundation Model for Chest X-ray Generation

no code implementations23 Nov 2022 Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier van der Sluijs, Małgorzata Połacin, Juan Manuel Zambrano Chaves, Tanishq Mathew Abraham, Shivanshu Purohit, Curtis P. Langlotz, Akshay Chaudhari

We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images, and that the output can be controlled to a new extent by using free-form text prompts including radiology-specific language.

Data Augmentation

Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains

no code implementations9 Oct 2022 Pierre Chambon, Christian Bluethgen, Curtis P. Langlotz, Akshay Chaudhari

Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches.

SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric

no code implementations NeurIPS Workshop Deep_Invers 2021 Philip M Adamson, Beliz Gunel, Jeffrey Dominic, Arjun D Desai, Daniel Spielman, Shreyas Vasanawala, John M. Pauly, Akshay Chaudhari

Self-supervised learning (SSL) has become a popular pre-training tool due to its ability to capture generalizable and domain-specific feature representations of the underlying data for downstream tasks.

MRI Reconstruction Self-Supervised Learning +1

Designing Counterfactual Generators using Deep Model Inversion

no code implementations NeurIPS 2021 Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias

Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models.

Image Generation

Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays

1 code implementation18 Feb 2021 Joseph Paul Cohen, Rupert Brooks, Sovann En, Evan Zucker, Anuj Pareek, Matthew P. Lungren, Akshay Chaudhari

We also found that the Latent Shift explanation allows a user to have more confidence in true positive predictions compared to traditional approaches (0. 15$\pm$0. 95 in a 5 point scale with p=0. 01) with only a small increase in false positive predictions (0. 04$\pm$1. 06 with p=0. 57).

MRSaiFE: Tissue Heating Prediction for MRI: a Feasibility Study

no code implementations1 Feb 2021 Simone Angela Winkler, Isabelle Saniour, Akshay Chaudhari, Fraser Robb, J Thomas Vaughan

We trained the software with a small database of image as a feasibility study and achieved successful proof of concept for both field strengths.


Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging

no code implementations7 Aug 2018 Akshay Chaudhari, Zhongnan Fang, Jin Hyung Lee, Garry Gold, Brian Hargreaves

Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications.


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