activity
20172020
most citedCheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

1.3k citations · 1.3k across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV20206 cited

Streaming Object Detection for 3-D Point Clouds

Wei Han, Zhengdong Zhang, Benjamin Caine +7

Autonomous vehicles operate in a dynamic environment, where the speed with which a vehicle can perceive and react impacts the safety and efficacy of the system. LiDAR provides a pr…

cs.CV2019

StarNet: Targeted Computation for Object Detection in Point Clouds

Jiquan Ngiam, Benjamin Caine, Wei Han +10

Detecting objects from LiDAR point clouds is an important component of self-driving car technology as LiDAR provides high resolution spatial information. Previous work on point-clo…

cs.CV2019

Using Videos to Evaluate Image Model Robustness

Keren Gu, Brandon Yang, Jiquan Ngiam +2

Human visual systems are robust to a wide range of image transformations that are challenging for artificial networks. We present the first study of image model robustness to the m…

cs.CV2019

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

Brandon Yang, Gabriel Bender, Quoc V. Le +1

Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples…

cs.CV20171.3k cited

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu +9

We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural networ…