Search Results for author:
Found 6 papers, 3 papers with code
OpenGlue: Open Source Graph Neural Net Based Pipeline for Image Matching
We present OpenGlue: a free open-source framework for image matching, that uses a Graph Neural Network-based matcher inspired by SuperGlue \cite{sarlin20superglue}.
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining
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.
LID 2020: The Learning from Imperfect Data Challenge Results
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.
Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images
First, we generate pseudo segmentation labels of abnormal regions in the training images through a supervised classification model enhanced with a regularization procedure.
NoPeopleAllowed: The Three-Step Approach to Weakly Supervised Semantic Segmentation
We propose a novel approach to weakly supervised semantic segmentation, which consists of three consecutive steps.
CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images
For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level.
