309 citations · 321 across the 5 of their papers we have counts for
7 papers
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification
Philip Chikontwe, Soopil Kim, Sang Hyun Park
Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples. Recent approaches pre-train a feature extr…
A Meta-Learning Approach for Medical Image Registration
Heejung Park, Gyeong Min Lee, Soopil Kim +4
Non-rigid registration is a necessary but challenging task in medical imaging studies. Recently, unsupervised registration models have shown good performance, but they often requir…
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs
Dongkyu Won, Euijin Jung, Sion An +2
Recently, Self-supervised learning methods able to perform image denoising without ground truth labels have been proposed. These methods create low-quality images by adding random…
Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing
Myeongkyun Kang, Philip Chikontwe, Miguel Luna +3
Following the pandemic outbreak, several works have proposed to diagnose COVID-19 with deep learning in computed tomography (CT); reporting performance on-par with experts. However…
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation
Soopil Kim, Sion An, Philip Chikontwe +1
Segmentation of organs of interest in 3D medical images is necessary for accurate diagnosis and longitudinal studies. Though recent advances using deep learning have shown success…
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification
Sion An, Soopil Kim, Philip Chikontwe +1
Brain-Computer Interfaces (BCI) based on Electroencephalography (EEG) signals, in particular motor imagery (MI) data have received a lot of attention and show the potential towards…