Daily Papers

Conversation agents powered by large language models are revolutionizing the way we interact with visual data. Recently, large vision-language models (LVLMs) have been extensively studied for both images and videos. However, these studies typically focus on common scenarios. In this work, we introduce an LVLM specifically designed for surgical scenarios. We integrate visual representations of surgical images and videos into the language feature space. Consequently, we establish a LVLM model, Surgical-LLaVA, fine-tuned on instruction following data of surgical scenarios. Our experiments demonstrate that Surgical-LLaVA exhibits impressive multi-modal chat abilities in surgical contexts, occasionally displaying multi-modal behaviors on unseen instructions. We conduct a quantitative evaluation of visual question-answering datasets for surgical scenarios. The results show superior performance compared to previous works, indicating the potential of our model to tackle more complex surgery scenarios.

Modern operating room is becoming increasingly complex, requiring innovative intra-operative support systems. While the focus of surgical data science has largely been on video analysis, integrating surgical computer vision with language capabilities is emerging as a necessity. Our work aims to advance Visual Question Answering (VQA) in the surgical context with scene graph knowledge, addressing two main challenges in the current surgical VQA systems: removing question-condition bias in the surgical VQA dataset and incorporating scene-aware reasoning in the surgical VQA model design. First, we propose a Surgical Scene Graph-based dataset, SSG-QA, generated by employing segmentation and detection models on publicly available datasets. We build surgical scene graphs using spatial and action information of instruments and anatomies. These graphs are fed into a question engine, generating diverse QA pairs. Our SSG-QA dataset provides a more complex, diverse, geometrically grounded, unbiased, and surgical action-oriented dataset compared to existing surgical VQA datasets. We then propose SSG-QA-Net, a novel surgical VQA model incorporating a lightweight Scene-embedded Interaction Module (SIM), which integrates geometric scene knowledge in the VQA model design by employing cross-attention between the textual and the scene features. Our comprehensive analysis of the SSG-QA dataset shows that SSG-QA-Net outperforms existing methods across different question types and complexities. We highlight that the primary limitation in the current surgical VQA systems is the lack of scene knowledge to answer complex queries. We present a novel surgical VQA dataset and model and show that results can be significantly improved by incorporating geometric scene features in the VQA model design. The source code and the dataset will be made publicly available at: https://github.com/CAMMA-public/SSG-QA

Tool tracking in surgical videos is essential for advancing computer-assisted interventions, such as skill assessment, safety zone estimation, and human-machine collaboration. However, the lack of context-rich datasets limits AI applications in this field. Existing datasets rely on overly generic tracking formalizations that fail to capture surgical-specific dynamics, such as tools moving out of the camera's view or exiting the body. This results in less clinically relevant trajectories and a lack of flexibility for real-world surgical applications. Methods trained on these datasets often struggle with visual challenges such as smoke, reflection, and bleeding, further exposing the limitations of current approaches. We introduce CholecTrack20, a specialized dataset for multi-class, multi-tool tracking in surgical procedures. It redefines tracking formalization with three perspectives: (i) intraoperative, (ii) intracorporeal, and (iii) visibility, enabling adaptable and clinically meaningful tool trajectories. The dataset comprises 20 full-length surgical videos, annotated at 1 fps, yielding over 35K frames and 65K labeled tool instances. Annotations include spatial location, category, identity, operator, phase, and scene visual challenge. Benchmarking state-of-the-art methods on CholecTrack20 reveals significant performance gaps, with current approaches (< 45\% HOTA) failing to meet the accuracy required for clinical translation. These findings motivate the need for advanced and intuitive tracking algorithms and establish CholecTrack20 as a foundation for developing robust AI-driven surgical assistance systems.

Recent advancements in surgical computer vision have been driven by vision-only models, which lack language semantics, relying on manually annotated videos to predict fixed object categories. This limits their generalizability to unseen surgical procedures and tasks. We propose leveraging surgical video lectures from e-learning platforms to provide effective vision and language supervisory signals for multi-modal representation learning, bypassing manual annotations. We address surgery-specific linguistic challenges using multiple automatic speech recognition systems for text transcriptions. We introduce SurgVLP - Surgical Vision Language Pre-training - a novel method for multi-modal representation learning. SurgVLP employs a new contrastive learning objective, aligning video clip embeddings with corresponding multiple text embeddings in a joint latent space. We demonstrate the representational capability of this space through several vision-and-language surgical tasks and vision-only tasks specific to surgery. Unlike current fully supervised approaches, SurgVLP adapts to different surgical procedures and tasks without specific fine-tuning, achieving zero-shot adaptation to tasks such as surgical tool, phase, and triplet recognition without manual annotation. These results highlight the transferability and versatility of the learned multi-modal representations in surgical video analysis. The code is available at https://github.com/CAMMA-public/SurgVLP

We investigate how both the adaptation of a generic foundation model via transfer learning and the integration of complementary modalities from the operating room (OR) can support surgical data science. To this end, we use V-JEPA as the single-modality foundation of a multimodal model for minimally invasive surgery support. We analyze how the model's downstream performance can benefit (a) from finetuning on unlabeled surgical video data and (b) from providing additional time-resolved data streams from the OR in a multimodal setup. In an in-house dataset of liver surgery videos, we analyze the tasks of predicting hospital length of stay and postoperative complications. In videos of the public HeiCo dataset, we analyze the task of surgical phase recognition. As a baseline, we apply pretrained V-JEPA to all tasks. We then finetune it on unlabeled, held-out videos to investigate its change in performance after domain adaptation. Following the idea of modular decision support networks, we integrate additional data streams from the OR by training a separate encoder to form a shared representation space with V-JEPA's embeddings. Our experiments show that finetuning on domain-specific data increases model performance. On the in-house data, integrating additional time-resolved data likewise benefits the model. On the HeiCo data, accuracy of the pretrained video-only, single-modality baseline setup is on par with the top-performing submissions of the EndoVis2017 challenge, while finetuning on domain-specific data increases accuracy further. Our results thus demonstrate how surgical data science can leverage public, generic foundation models. Likewise, they indicate the potential of domain adaptation and of integrating suitable complementary data streams from the OR. To support further research, we release our code and model weights at https://github.com/DigitalSurgeryLab-Basel/ML-CDS-2025.

Most studies on surgical activity recognition utilizing Artificial intelligence (AI) have focused mainly on recognizing one type of activity from small and mono-centric surgical video datasets. It remains speculative whether those models would generalize to other centers. In this work, we introduce a large multi-centric multi-activity dataset consisting of 140 videos (MultiBypass140) of laparoscopic Roux-en-Y gastric bypass (LRYGB) surgeries performed at two medical centers: the University Hospital of Strasbourg (StrasBypass70) and Inselspital, Bern University Hospital (BernBypass70). The dataset has been fully annotated with phases and steps. Furthermore, we assess the generalizability and benchmark different deep learning models in 7 experimental studies: 1) Training and evaluation on BernBypass70; 2) Training and evaluation on StrasBypass70; 3) Training and evaluation on the MultiBypass140; 4) Training on BernBypass70, evaluation on StrasBypass70; 5) Training on StrasBypass70, evaluation on BernBypass70; Training on MultiBypass140, evaluation 6) on BernBypass70 and 7) on StrasBypass70. The model's performance is markedly influenced by the training data. The worst results were obtained in experiments 4) and 5) confirming the limited generalization capabilities of models trained on mono-centric data. The use of multi-centric training data, experiments 6) and 7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments 1) and 2)). MultiBypass140 shows considerable variation in surgical technique and workflow of LRYGB procedures between centers. Therefore, generalization experiments demonstrate a remarkable difference in model performance. These results highlight the importance of multi-centric datasets for AI model generalization to account for variance in surgical technique and workflows.

Purpose: Surgical performance depends not only on surgeons' technical skills but also on team communication within and across the different professional groups present during the operation. Therefore, automatically identifying team communication in the OR is crucial for patient safety and advances in the development of computer-assisted surgical workflow analysis and intra-operative support systems. To take the first step, we propose a new task of detecting communication briefings involving all OR team members, i.e. the team Time-out and the StOP?-protocol, by localizing their start and end times in video recordings of surgical operations. Methods: We generate an OR dataset of real surgeries, called Team-OR, with more than one hundred hours of surgical videos captured by the multi-view camera system in the OR. The dataset contains temporal annotations of 33 Time-out and 22 StOP?-protocol activities in total. We then propose a novel group activity detection approach, where we encode both scene context and action features, and use an efficient neural network model to output the results. Results: The experimental results on the Team-OR dataset show that our approach outperforms existing state-of-the-art temporal action detection approaches. It also demonstrates the lack of research on group activities in the OR, proving the significance of our dataset. Conclusion: We investigate the Team Time-Out and the StOP?-protocol in the OR, by presenting the first OR dataset with temporal annotations of group activities protocols, and introducing a novel group activity detection approach that outperforms existing approaches. Code is available at https://github.com/CAMMA-public/Team-OR.