| Jun 29, 2025 | 🩺 Excited to share that our paper “Breast density in MRI: an AI-based quantification and relationship to assessment in mammography” has been accepted for publication in npj Breast Cancer! In this study, we developed an AI-driven pipeline to quantify breast density directly from MRI scans and explored how these MRI-derived metrics relate to traditional density assessments from mammography. Our work provides new insights into leveraging MRI for more comprehensive breast density evaluation. Huge thanks to all co-authors and collaborators — looking forward to seeing this work published soon! |
| Jun 29, 2025 | 🎉 Excited to share that the paper I contributed as co-author “SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images” has been accepted to ICCV 2025! In this work, we propose SAMora, a novel framework that boosts the performance of the Segment Anything Model (SAM) on medical images through a hierarchical self-supervised pre-training strategy. Our approach brings significant improvements to medical segmentation tasks where labeled data is scarce. Thank you to all co-authors and collaborators for making this possible！ |
| Jun 25, 2025 | 🧠 Excited to share our latest work “MRI-CORE: A Foundation Model for Magnetic Resonance Imaging”, now available on arXiv! This project introduces a general-purpose MRI foundation model capable of handling diverse MRI sequences and anatomical regions, laying the groundwork for improved downstream tasks across medical imaging applications. 📄 Read the paper on arXiv Thank you to all collaborators for making this happen! |
| Jun 12, 2025 | 🎉 Our paper “Deep Learning Automates Cobb Angle Measurement Compared with Multi-Expert Observers” has been accepted to the British Journal of Radiology: Artificial Intelligence! This work presents a clinically interpretable AI model for scoliosis assessment, validated in a multi-reader study against experienced clinicians. Big thanks to our collaborators! |
| May 13, 2025 | 📌 Our paper “How to Build the Best Medical Image Segmentation Algorithm Using Foundation Models” has been accepted to the Journal of Machine Learning for Biomedical Imaging! We benchmarked 18+ fine-tuning strategies for adapting SAM in medical imaging and open-sourced our pipeline. Proud to contribute to the community! |
| Mar 12, 2025 | 🦴 Thrilled to announce that “SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI” has been accepted to Medical Image Analysis! This work presents the first generalized MRI bone segmentation model across anatomical regions and MRI sequences. Thank you to all collaborators! |