1 code implementation • 19 Apr 2022 • Ostap Viniavskyi, Mariia Dobko, Dmytro Mishkin, Oles Dobosevych
We present OpenGlue: a free open-source framework for image matching, that uses a Graph Neural Network-based matcher inspired by SuperGlue \cite{sarlin20superglue}.
no code implementations • 15 Oct 2021 • Mariia Dobko, Danylo-Ivan Kolinko, Ostap Viniavskyi, Yurii Yelisieiev
Inspired by a nnU-Net framework we decided to combine it with our modified TransBTS by changing the architecture inside nnU-Net to our custom model.
no code implementations • 17 Oct 2020 • Yunchao Wei, Shuai Zheng, Ming-Ming Cheng, Hang Zhao, LiWei Wang, Errui Ding, Yi Yang, Antonio Torralba, Ting Liu, Guolei Sun, Wenguan Wang, Luc van Gool, Wonho Bae, Junhyug Noh, Jinhwan Seo, Gunhee Kim, Hao Zhao, Ming Lu, Anbang Yao, Yiwen Guo, Yurong Chen, Li Zhang, Chuangchuang Tan, Tao Ruan, Guanghua Gu, Shikui Wei, Yao Zhao, Mariia Dobko, Ostap Viniavskyi, Oles Dobosevych, Zhendong Wang, Zhenyuan Chen, Chen Gong, Huanqing Yan, Jun He
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
1 code implementation • 1 Jul 2020 • Ostap Viniavskyi, Mariia Dobko, Oles Dobosevych
First, we generate pseudo segmentation labels of abnormal regions in the training images through a supervised classification model enhanced with a regularization procedure.
no code implementations • 13 Jun 2020 • Mariia Dobko, Ostap Viniavskyi, Oles Dobosevych
We propose a novel approach to weakly supervised semantic segmentation, which consists of three consecutive steps.
1 code implementation • 23 Jan 2020 • Mariia Dobko, Bohdan Petryshak, Oles Dobosevych
For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level.