Search Results for author: Meet Shah

Found 8 papers, 1 papers with code

A deep learning algorithm for reducing false positives in screening mammography

no code implementations13 Apr 2022 Stefano Pedemonte, Trevor Tsue, Brent Mombourquette, Yen Nhi Truong Vu, Thomas Matthews, Rodrigo Morales Hoil, Meet Shah, Nikita Ghare, Naomi Zingman-Daniels, Susan Holley, Catherine M. Appleton, Jason Su, Richard L. Wahl

This work lays the foundation for semi-autonomous breast cancer screening systems that could benefit patients and healthcare systems by reducing false positives, unnecessary procedures, patient anxiety, and expenses.

LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion

no code implementations2 Oct 2020 Meet Shah, Zhiling Huang, Ankit Laddha, Matthew Langford, Blake Barber, Sidney Zhang, Carlos Vallespi-Gonzalez, Raquel Urtasun

In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps.

Trajectory Prediction

Conditional Entropy Coding for Efficient Video Compression

no code implementations ECCV 2020 Jerry Liu, Shenlong Wang, Wei-Chiu Ma, Meet Shah, Rui Hu, Pranaab Dhawan, Raquel Urtasun

We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames.


A Hypersensitive Breast Cancer Detector

no code implementations23 Jan 2020 Stefano Pedemonte, Brent Mombourquette, Alexis Goh, Trevor Tsue, Aaron Long, Sadanand Singh, Thomas Paul Matthews, Meet Shah, Jason Su

In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity.

Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

no code implementations23 Jan 2020 Sadanand Singh, Thomas Paul Matthews, Meet Shah, Brent Mombourquette, Trevor Tsue, Aaron Long, Ranya Almohsen, Stefano Pedemonte, Jason Su

In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images.


Cycle-Consistency for Robust Visual Question Answering

no code implementations CVPR 2019 Meet Shah, Xinlei Chen, Marcus Rohrbach, Devi Parikh

Despite significant progress in Visual Question Answering over the years, robustness of today's VQA models leave much to be desired.

Question Answering Question Generation +2

Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks

no code implementations29 Dec 2018 Yash Bhalgat, Meet Shah, Suyash Awate

For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in terms of costs, time and logistics involved.

Image Segmentation Medical Image Segmentation +1

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