Congratulations to our research fellow Zhennong Chen for receiving the RSNA Trainee Research Prize (1 of 3 awardees in Physics track) for his outstanding work on diffusion model for motion correction in portable photon-counting CT!
LLM-driven Multimodal Target Volume Contouring in Radiation Oncology
Excited to share our latest research “LLM-driven multimodal target volume contouring in radiation oncology” published in Nature Communications by CAMCA’s Yujin Oh. Check it out here: https://www.nature.com/articles/s41467-024-53387-y In this study, we introduce LLMSeg, a novel multimodal AI model driven by
BiomedGPT, an open source and computing-friendly generalist vision-language model is shown with promising performance in a series of potential clinical applications.
The new study, conducted in collaboration across multiple institutions, lays out a generalist medical AI model, BiomedGPT, to support multiple potential clinical applications. By evaluating on 25 datasets across 9 biomedical tasks and different modalities, BiomedGPT achived 16 state-of-the-art results.
Upcoming AHA Scientific Session 2024 Presentation!
We are excited to share that our work “Adaptation of Prompt-enabled Segment-Anything-Model Enhance the Accuracy and Generalizability of Cine Cardiac Magnetic Resonance Segmentation” by Zhennong Chen, Sekeun Kim, Hui Ren and Xiang Li has been accepted for presentation at American
Can AI solve the clinical data problem?
CAMCA’s, Xiang Li and Quanzheng Li, recently collaborated with Daniel Barron to create a blog post for Science. This paper discusses how, AI can help clinical data analytics across different stages of the healthcare lifecycle and in various roles. Insights
Siyeop Yoon, 3 papers accepted at the MICCAI 2024 main conference!
We are pleased to share that three of Siyeop Yoon’s papers have been accepted at the MICCAI 2024 main conference! MICCAI is one of the most respected conferences in the field of medical image analysis. 1. “Efficient Volumetric Conditional Score-based
CAMCA’s Newest Publication “Texture-preserving low dose CT image denoising using Pearson divergence”
Our latest paper, “Texture-preserving low dose CT image denoising using Pearson divergence” introduces a novel approach to enhance texture in low-dose CT images. Previous methods using MSE often had over-smoothed edges and degradation of texture in images, but our proposed
Matthew Tivnan, Early acceptance to MICCAI 2024 for “Hallucination Index: An Image Quality Metric for Generative Reconstruction Models”.
CAMCA Investigator, Matthew Tivnan, got an early acceptance to MICCAI 2024 for his work titled “Hallucination Index: An Image Quality Metric for Generative Reconstruction Models”. The key innovation in this work is to define what hallucinations ARE by defining what
2024 Sejong Science Fellowship Awarded to Post Doc at CAMCA!
Yujin Oh has been awarded the prestigious 2024 Sejong Science Fellowship by the Ministry of Science and ICT (MSIT) in Korea. This fellowship is awarded to outstanding young investigators in Korea. She will receive approximately $50,000 USD for one year
MMMI/ML4MHD Workshop
This year, we’re thrilled to announce the continuation of the MICCAI workshop on Multiscale Multimodal Medical Imaging (MMMI 2024), alongside the 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data (ML4MHD 2024). For more information click this link: https://mmmi2024.github.io Thank