578 citations · 735 across the 25 of their papers we have counts for
12 papers · 1 filter
From Panel to Pixel: Zoom-In Vision-Language Pretraining from Biomedical Scientific Literature
Kun Yuan, Min Woo Sun, Zhen Chen +7
There is a growing interest in developing strong biomedical vision-language models. A popular approach to achieve robust representations is to use web-scale scientific data. Howeve…
DSeq-JEPA: Discriminative Sequential Joint-Embedding Predictive Architecture
Xiangteng He, Shunsuke Sakai, Shivam Chandhok +5
Recent advances in self-supervised visual representation learning have demonstrated the effectiveness of predictive latent-space objectives for learning transferable features. In p…
End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data
Farahdiba Zarin, Nicolas Padoy, Jérémy Dana +1
The fine-grained surface reconstruction of different organs from 3D medical imaging can provide advanced diagnostic support and improved surgical planning. However, the representat…
Recognizing Surgical Phases Anywhere: Few-Shot Test-time Adaptation and Task-graph Guided Refinement
Kun Yuan, Tingxuan Chen, Shi Li +9
The complexity and diversity of surgical workflows, driven by heterogeneous operating room settings, institutional protocols, and anatomical variability, present a significant chal…
Adaptation of Multi-modal Representation Models for Multi-task Surgical Computer Vision
Soham Walimbe, Britty Baby, Vinkle Srivastav +1
Surgical AI often involves multiple tasks within a single procedure, like phase recognition or assessing the Critical View of Safety in laparoscopic cholecystectomy. Traditional mo…
Multi-modal Representations for Fine-grained Multi-label Critical View of Safety Recognition
Britty Baby, Vinkle Srivastav, Pooja P. Jain +3
The Critical View of Safety (CVS) is crucial for safe laparoscopic cholecystectomy, yet assessing CVS criteria remains a complex and challenging task, even for experts. Traditional…
Learning from Sparse Point Labels for Dense Carcinosis Localization in Advanced Ovarian Cancer Assessment
Farahdiba Zarin, Riccardo Oliva, Vinkle Srivastav +7
Learning from sparse labels is a challenge commonplace in the medical domain. This is due to numerous factors, such as annotation cost, and is especially true for newly introduced…
SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
Yiming Huang, Long Bai, Beilei Cui +6
In contemporary surgical research and practice, accurately comprehending 3D surgical scenes with text-promptable capabilities is particularly crucial for surgical planning and real…
SurgVidLM: Towards Multi-grained Surgical Video Understanding with Large Language Model
Guankun Wang, Junyi Wang, Wenjin Mo +11
Surgical scene understanding is critical for surgical training and robotic decision-making in robot-assisted surgery. Recent advances in Multimodal Large Language Models (MLLMs) ha…
Text-driven Adaptation of Foundation Models for Few-shot Surgical Workflow Analysis
Tingxuan Chen, Kun Yuan, Vinkle Srivastav +2
Purpose: Surgical workflow analysis is crucial for improving surgical efficiency and safety. However, previous studies rely heavily on large-scale annotated datasets, posing challe…
Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment
Yaling Shen, Zhixiong Zhuang, Kun Yuan +4
Medical multimodal large language models (MLLMs) are becoming an instrumental part of healthcare systems, assisting medical personnel with decision making and results analysis. Mod…
EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery
Guankun Wang, Long Bai, Junyi Wang +13
Recently, Multimodal Large Language Models (MLLMs) have demonstrated their immense potential in computer-aided diagnosis and decision-making. In the context of robotic-assisted sur…