86 citations · 95 across the 10 of their papers we have counts for
12 papers
Mixture of Multi-Sequence Feature Difference for Contrast-Enhanced MRI Synthesis
Jonghun Kim, Junmo Kwon, Hyunjin Park
Contrast-enhanced magnetic resonance imaging (CE-MRI) is crucial for accurately diagnosing and segmenting brain tumors. However, the acquisition of CE-MRI requires injecting contra…
Multi-LLM Collaborative MRI Report Generation for Visual Instruction Tuning in Brain Oncology
Sinyoung Ra, Jonghun Kim, Hyunjin Park
Recent advances in large language models (LLMs) and their extension to vision-language models (VLMs) have made it easier to combine text and images for tasks such as report generat…
Visual Instruction-Finetuned Language Model for Versatile Brain MR Image Tasks
Jonghun Kim, Sinyoung Ra, Hyunjin Park
LLMs have demonstrated remarkable capabilities in linguistic reasoning and are increasingly adept at vision-language tasks. The integration of image tokens into transformers has en…
Simulating Post-Neoadjuvant Chemotherapy Breast Cancer MRI via Diffusion Model with Prompt Tuning
Jonghun Kim, Hyunjin Park
Neoadjuvant chemotherapy (NAC) is a common therapy option before the main surgery for breast cancer. Response to NAC is monitored using follow-up dynamic contrast-enhanced magnetic…
Tumor Synthesis conditioned on Radiomics
Jonghun Kim, Inye Na, Eun Sook Ko +1
Due to privacy concerns, obtaining large datasets is challenging in medical image analysis, especially with 3D modalities like Computed Tomography (CT) and Magnetic Resonance Imagi…
RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features
Inye Na, Nejung Rue, Jiwon Chung +1
Medical image retrieval is a valuable field for supporting clinical decision-making, yet current methods primarily support 2D images and require fully annotated queries, limiting c…
Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference
Jonghun Kim, Gyeongdeok Jo, Sinyoung Ra +1
Medical imaging data contain sensitive patient information requiring strong privacy protection. Many analytical setups require data to be sent to a server for inference purposes. H…
Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI
Jonghun Kim, Mansu Kim, Hyunjin Park
The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models wit…
RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Features
Inye Na, Jonghun Kim, Eun Sook Ko +1
Motivated by the question, "Can we generate tumors with desired attributes?'' this study leverages radiomics features to explore the feasibility of generating synthetic tumor image…
Radiomics-guided Multimodal Self-attention Network for Predicting Pathological Complete Response in Breast MRI
Jonghun Kim, Hyunjin Park
Breast cancer is the most prevalent cancer among women and predicting pathologic complete response (pCR) after anti-cancer treatment is crucial for patient prognosis and treatment…
Synthetic Tumor Manipulation: With Radiomics Features
Inye Na, Jonghun Kim, Hyunjin Park
We introduce RadiomicsFill, a synthetic tumor generator conditioned on radiomics features, enabling detailed control and individual manipulation of tumor subregions. This condition…
Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study
Jonghun Kim, Hyunjin Park
Multi-modal images play a crucial role in comprehensive evaluations in medical image analysis providing complementary information for identifying clinically important biomarkers. H…