activity
20192022
most citedUNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

76 citations · 123 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20225 cited

AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models

Xiaofan Zhang, Zongwei Zhou, Deming Chen +1

Recently, large pre-trained models have significantly improved the performance of various Natural LanguageProcessing (NLP) tasks but they are expensive to serve due to long serving…

eess.IV2021

Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection

Nahid Ul Islam, Shiv Gehlot, Zongwei Zhou +2

Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstr…

cs.CV20207 cited

Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou +2

Medical images are naturally associated with rich semantics about the human anatomy, reflected in an abundance of recurring anatomical patterns, offering unique potential to foster…

eess.IV202076 cited

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh +1

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (…

cs.LG201935 cited

Scale MLPerf-0.6 models on Google TPU-v3 Pods

Sameer Kumar, Victor Bitorff, Dehao Chen +9

The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0.6 training benchmark demonstrates the scalability of a suite of industry relevant ML models. MLPerf defin…