27 citations · 57 across the 5 of their papers we have counts for
6 papers
Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation
Sangjoon Park, Jong Chul Ye
The widespread application of artificial intelligence in health research is currently hampered by limitations in data availability. Distributed learning methods such as federated l…
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training
Sangjoon Park, Gwanghyun Kim, Jeongsol Kim +2
Federated learning, which shares the weights of the neural network across clients, is gaining attention in the healthcare sector as it enables training on a large corpus of decentr…
Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification
Sangjoon Park, Gwanghyun Kim, Yujin Oh +6
Developing a robust algorithm to diagnose and quantify the severity of COVID-19 using Chest X-ray (CXR) requires a large number of well-curated COVID-19 datasets, which is difficul…
Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer
Gwanghyun Kim, Sangjoon Park, Yujin Oh +6
Under the global pandemic of COVID-19, building an automated framework that quantifies the severity of COVID-19 and localizes the relevant lesion on chest X-ray images has become i…
Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus
Sangjoon Park, Gwanghyun Kim, Yujin Oh +6
Under the global COVID-19 crisis, developing robust diagnosis algorithm for COVID-19 using CXR is hampered by the lack of the well-curated COVID-19 data set, although CXR data with…
Deep Learning COVID-19 Features on CXR using Limited Training Data Sets
Yujin Oh, Sangjoon Park, Jong Chul Ye
Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfort…