11 citations · 18 across the 5 of their papers we have counts for
6 papers
ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation
Yu Xin, Gorkem Can Ates, Jun Ma +5
Text guided 3D medical image segmentation offers a flexible alternative to class based and spatial prompt based models by allowing users to specify regions of interest directly in…
TripleSumm: Adaptive Triple-Modality Fusion for Video Summarization
Sumin Kim, Hyemin Jeong, Mingu Kang +3
The exponential growth of video content necessitates effective video summarization to efficiently extract key information from long videos. However, current approaches struggle to…
SummDiff: Generative Modeling of Video Summarization with Diffusion
Kwanseok Kim, Jaehoon Hahm, Sumin Kim +3
Video summarization is a task of shortening a video by choosing a subset of frames while preserving its essential moments. Despite the innate subjectivity of the task, previous wor…
MedSAM2: Segment Anything in 3D Medical Images and Videos
Jun Ma, Zongxin Yang, Sumin Kim +6
Medical image and video segmentation is a critical task for precision medicine, which has witnessed considerable progress in developing task or modality-specific and generalist mod…
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Segment Anything in Medical Images and Videos: Benchmark and Deployment
Jun Ma, Sumin Kim, Feifei Li +4
Recent advances in segmentation foundation models have enabled accurate and efficient segmentation across a wide range of natural images and videos, but their utility to medical da…