no code implementations • 28 Aug 2023 • Bing Han, Junyu Dai, Weituo Hao, Xinyan He, Dong Guo, Jitong Chen, Yuxuan Wang, Yanmin Qian, Xuchen Song
We tested InstructME in instrument-editing, remixing, and multi-round editing.
no code implementations • 21 Oct 2021 • Imanol Luengo, Maria Grammatikopoulou, Rahim Mohammadi, Chris Walsh, Chinedu Innocent Nwoye, Deepak Alapatt, Nicolas Padoy, Zhen-Liang Ni, Chen-Chen Fan, Gui-Bin Bian, Zeng-Guang Hou, Heonjin Ha, Jiacheng Wang, Haojie Wang, Dong Guo, Lu Wang, Guotai Wang, Mobarakol Islam, Bharat Giddwani, Ren Hongliang, Theodoros Pissas, Claudio Ravasio, Martin Huber, Jeremy Birch, Joan M. Nunez Do Rio, Lyndon Da Cruz, Christos Bergeles, Hongyu Chen, Fucang Jia, Nikhil KumarTomar, Debesh Jha, Michael A. Riegler, Pal Halvorsen, Sophia Bano, Uddhav Vaghela, Jianyuan Hong, Haili Ye, Feihong Huang, Da-Han Wang, Danail Stoyanov
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
no code implementations • 29 Dec 2020 • Lu Wang, Dong Guo, Guotai Wang, Shaoting Zhang
In this paper, we propose an annotation-efficient learning framework for segmentation tasks that avoids annotations of training images, where we use an improved Cycle-Consistent Generative Adversarial Network (GAN) to learn from a set of unpaired medical images and auxiliary masks obtained either from a shape model or public datasets.
no code implementations • 23 Mar 2020 • Tobias Ross, Annika Reinke, Peter M. Full, Martin Wagner, Hannes Kenngott, Martin Apitz, Hellena Hempe, Diana Mindroc Filimon, Patrick Scholz, Thuy Nuong Tran, Pierangela Bruno, Pablo Arbeláez, Gui-Bin Bian, Sebastian Bodenstedt, Jon Lindström Bolmgren, Laura Bravo-Sánchez, Hua-Bin Chen, Cristina González, Dong Guo, Pål Halvorsen, Pheng-Ann Heng, Enes Hosgor, Zeng-Guang Hou, Fabian Isensee, Debesh Jha, Tingting Jiang, Yueming Jin, Kadir Kirtac, Sabrina Kletz, Stefan Leger, Zhixuan Li, Klaus H. Maier-Hein, Zhen-Liang Ni, Michael A. Riegler, Klaus Schoeffmann, Ruohua Shi, Stefanie Speidel, Michael Stenzel, Isabell Twick, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Wang, Yu-Jie Zhang, Yan-Jie Zhou, Lei Zhu, Manuel Wiesenfarth, Annette Kopp-Schneider, Beat P. Müller-Stich, Lena Maier-Hein
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
no code implementations • 2 May 2018 • Ayush Jaiswal, Dong Guo, Cauligi S. Raghavendra, Paul Thompson
Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data.
no code implementations • 9 Oct 2017 • Wenzhe Li, Dong Guo, Greg Ver Steeg, Aram Galstyan
Many real-world networks are complex dynamical systems, where both local (e. g., changing node attributes) and global (e. g., changing network topology) processes unfold over time.
no code implementations • 13 Jan 2017 • Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha
First, in order to reduce the number of random features required by kernel models, we propose a simple but effective method for feature selection.
no code implementations • 18 Mar 2016 • Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani, Kuan Liu, Avner May, Aurelien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition.
no code implementations • 14 Nov 2014 • Zhiyun Lu, Avner May, Kuan Liu, Alireza Bagheri Garakani, Dong Guo, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha
The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 13 Nov 2014 • Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan
We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots.