How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks

no code implementations • 4 Apr 2024 • Dongang Wang, Peilin Liu, Hengrui Wang, Heidi Beadnall, Kain Kyle, Linda Ly, Mariano Cabezas, Geng Zhan, Ryan Sullivan, Weidong Cai, Wanli Ouyang, Fernando Calamante, Michael Barnett, Chenyu Wang

This paper focuses on an early stage phase of deep learning research, prior to model development, and proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks.

MRI segmentation

Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training

no code implementations • 31 Aug 2023 • Lei Bai, Dongang Wang, Michael Barnett, Mariano Cabezas, Weidong Cai, Fernando Calamante, Kain Kyle, Dongnan Liu, Linda Ly, Aria Nguyen, Chun-Chien Shieh, Ryan Sullivan, Hengrui Wang, Geng Zhan, Wanli Ouyang, Chenyu Wang

Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction.

Federated Learning Lesion Segmentation

Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation

no code implementations • 8 Feb 2023 • Geng Zhan, Dongang Wang, Mariano Cabezas, Lei Bai, Kain Kyle, Wanli Ouyang, Michael Barnett, Chenyu Wang

An accurate and robust quantitative measurement of brain volume change is paramount for translational research and clinical applications.

MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning

no code implementations • 3 May 2022 • Dongnan Liu, Mariano Cabezas, Dongang Wang, Zihao Tang, Lei Bai, Geng Zhan, Yuling Luo, Kain Kyle, Linda Ly, James Yu, Chun-Chien Shieh, Aria Nguyen, Ettikan Kandasamy Karuppiah, Ryan Sullivan, Fernando Calamante, Michael Barnett, Wanli Ouyang, Weidong Cai, Chenyu Wang

In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.

Federated Learning Lesion Segmentation +1

Dividing and Aggregating Network for Multi-view Action Recognition

no code implementations • ECCV 2018 • Dongang Wang, Wanli Ouyang, Wen Li, Dong Xu

We then train view-specific action classifiers based on the view-specific representation for each view and a view classifier based on the shared representation at lower layers.

Action Recognition Temporal Action Localization

Cross-media Event Extraction and Recommendation

no code implementations • NAACL 2016 • Di Lu, Clare Voss, Fangbo Tao, Xiang Ren, Rachel Guan, Rostyslav Korolov, Tongtao Zhang, Dongang Wang, Hongzhi Li, Taylor Cassidy, Heng Ji, Shih-Fu Chang, Jiawei Han, William Wallace, James Hendler, Mei Si, Lance Kaplan

Event Extraction

EventNet Version 1.1 Technical Report

no code implementations • 24 May 2016 • Dongang Wang, Zheng Shou, Hongyi Liu, Shih-Fu Chang

Finally, EventNet version 1. 1 contains 67, 641 videos, 500 events, and 5, 028 event-specific concepts.

Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs

1 code implementation • CVPR 2016 • Zheng Shou, Dongang Wang, Shih-Fu Chang

To address this challenging issue, we exploit the effectiveness of deep networks in temporal action localization via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes on the learned classification network to localize each action instance.

Action Classification Classification +3