Search Results for author: Sravanthi Parasa

Found 7 papers, 6 papers with code

Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations

no code implementations23 Mar 2022 Steven Hicks, Andrea Storås, Michael Riegler, Cise Midoglu, Malek Hammou, Thomas de Lange, Sravanthi Parasa, Pål Halvorsen, Inga Strümke

Deep learning has in recent years achieved immense success in all areas of computer vision and has the potential of assisting medical doctors in analyzing visual content for disease and other abnormalities.

Computer Vision Explainable artificial intelligence

Literature-Augmented Clinical Outcome Prediction

1 code implementation16 Nov 2021 Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope

We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models.

Decision Making

A Search Engine for Discovery of Scientific Challenges and Directions

1 code implementation NeurIPS Workshop AI4Scien 2021 Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld, Tom Hope

To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery.

SinGAN-Seg: Synthetic training data generation for medical image segmentation

1 code implementation29 Jun 2021 Vajira Thambawita, Pegah Salehi, Sajad Amouei Sheshkal, Steven A. Hicks, Hugo L. Hammer, Sravanthi Parasa, Thomas de Lange, Pål Halvorsen, Michael A. Riegler

The pipeline is evaluated using qualitative and quantitative comparisons between real and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited.

Medical Image Segmentation Semantic Segmentation +2

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

3 code implementations NAACL 2021 Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge.

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