8 citations · 10 across the 3 of their papers we have counts for
6 papers
OpenGlue: Open Source Graph Neural Net Based Pipeline for Image Matching
Ostap Viniavskyi, Mariia Dobko, Dmytro Mishkin +1
We present OpenGlue: a free open-source framework for image matching, that uses a Graph Neural Network-based matcher inspired by SuperGlue \cite{sarlin20superglue}. We show that in…
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining
Mariia Dobko, Danylo-Ivan Kolinko, Ostap Viniavskyi +1
We apply an ensemble of modified TransBTS, nnU-Net, and a combination of both for the segmentation task of the BraTS 2021 challenge. In fact, we change the original architecture of…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images
Ostap Viniavskyi, Mariia Dobko, Oles Dobosevych
Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that…
NoPeopleAllowed: The Three-Step Approach to Weakly Supervised Semantic Segmentation
Mariia Dobko, Ostap Viniavskyi, Oles Dobosevych
We propose a novel approach to weakly supervised semantic segmentation, which consists of three consecutive steps. The first two steps extract high-quality pseudo masks from image-…
CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images
Mariia Dobko, Bohdan Petryshak, Oles Dobosevych
To decrease patient waiting time for diagnosis of the Coronary Artery Disease, automatic methods are applied to identify its severity using Coronary Computed Tomography Angiography…