30 citations · 44 across the 14 of their papers we have counts for
15 papers
CoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support
Yichen Wu, Kailong Fan, Sangjoon Park +9
Recent multi-agent frameworks have shown promise for oncology decision support, yet most assume centralized data access and rely on prompt-based assignment, limiting their applicab…
SAMed-2: Selective Memory Enhanced Medical Segment Anything Model
Zhiling Yan, Sifan Song, Dingjie Song +11
Recent "segment anything" efforts show promise by learning from large-scale data, but adapting such models directly to medical images remains challenging due to the complexity of m…
Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics
Siyeop Yoon, Sifan Song, Pengfei Jin +6
We propose a cascaded 3D diffusion model framework to synthesize high-fidelity 3D PET/CT volumes directly from demographic variables, addressing the growing need for realistic digi…
OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging
Sifan Song, Siyeop Yoon, Pengfei Jin +10
Recent advances in representation learning often rely on holistic embeddings that entangle multiple semantic components, limiting interpretability and generalization. These issues…
Prediction of Frozen Region Growth in Kidney Cryoablation Intervention Using a 3D Flow-Matching Model
Siyeop Yoon, Yujin Oh, Matthew Tivnan +7
This study presents a 3D flow-matching model designed to predict the progression of the frozen region (iceball) during kidney cryoablation. Precise intraoperative guidance is criti…
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
Yujin Oh, Pengfei Jin, Sangjoon Park +6
Ensuring fairness in medical image segmentation is critical due to biases in imbalanced clinical data acquisition caused by demographic attributes (e.g., age, sex, race) and clinic…
ECHOPulse: ECG controlled echocardio-grams video generation
Yiwei Li, Sekeun Kim, Zihao Wu +8
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clin…
EchoFM: Foundation Model for Generalizable Echocardiogram Analysis
Sekeun Kim, Pengfei Jin, Sifan Song +6
Foundation models have recently gained significant attention because of their generalizability and adaptability across multiple tasks and data distributions. Although medical found…
Beyond Adapter Retrieval: Latent Geometry-Preserving Composition via Sparse Task Projection
Pengfei Jin, Peng Shu, Sifan Song +6
Recent advances in parameter-efficient transfer learning have demonstrated the utility of composing LoRA adapters from libraries of pretrained modules. However, most existing appro…
Autonomous Robotic Ultrasound System for Liver Follow-up Diagnosis: Pilot Phantom Study
Tianpeng Zhang, Sekeun Kim, Jerome Charton +4
The paper introduces a novel autonomous robot ultrasound (US) system targeting liver follow-up scans for outpatients in local communities. Given a computed tomography (CT) image wi…
Prompt-driven Universal Model for View-Agnostic Echocardiography Analysis
Sekeun Kim, Hui Ren, Peng Guo +5
Echocardiography segmentation for cardiac analysis is time-consuming and resource-intensive due to the variability in image quality and the necessity to process scans from various…
Temporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation
Zhennong Chen, Sekeun Kim, Hui Ren +2
Accurate myocardium segmentation across all phases in one cardiac cycle in cine cardiac magnetic resonance (CMR) scans is crucial for comprehensively cardiac function analysis. Des…
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
Cheng Chen, Juzheng Miao, Dufan Wu +10
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation ta…
MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography
Sekeun Kim, Pengfei Jin, Cheng Chen +9
Despite achieving impressive results in general-purpose semantic segmentation with strong generalization on natural images, the Segment Anything Model (SAM) has shown less precisio…
Radiology-Llama2: Best-in-Class Large Language Model for Radiology
Zhengliang Liu, Yiwei Li, Peng Shu +18
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning. Radiology-Llama2 is based on the Llama2 arch…