75 citations · 136 across the 9 of their papers we have counts for
11 papers
Read Like a Radiologist: Efficient Vision-Language Model for 3D Medical Imaging Interpretation
Changsun Lee, Sangjoon Park, Cheong-Il Shin +4
Recent medical vision-language models (VLMs) have shown promise in 2D medical image interpretation. However extending them to 3D medical imaging has been challenging due to computa…
LLM-driven Multimodal Target Volume Contouring in Radiation Oncology
Yujin Oh, Sangjoon Park, Hwa Kyung Byun +4
Target volume contouring for radiation therapy is considered significantly more challenging than the normal organ segmentation tasks as it necessitates the utilization of both imag…
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model
Jaeyoung Huh, Sangjoon Park, Jeong Eun Lee +1
Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications o…
Single-round Self-supervised Distributed Learning using Vision Transformer
Sangjoon Park, Ik-Jae Lee, Jun Won Kim +1
Despite the recent success of deep learning in the field of medicine, the issue of data scarcity is exacerbated by concerns about privacy and data ownership. Distributed learning a…
Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in Radiology
Sangjoon Park, Eun Sun Lee, Kyung Sook Shin +2
Oversight AI is an emerging concept in radiology where the AI forms a symbiosis with radiologists by continuously supporting radiologists in their decision-making. Recent advances…
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…