Slot-BERT: Self-supervised Object Discovery in Surgical Video

• URL: http://arxiv.org/abs/2501.12477v2
• Date: Mon, 27 Jan 2025 19:53:35 GMT
• Title: Slot-BERT: Self-supervised Object Discovery in Surgical Video
• Authors: Guiqiu Liao, Matjaz Jogan, Marcel Hussing, Kenta Nakahashi, Kazuhiro Yasufuku, Amin Madani, Eric Eaton, Daniel A. Hashimoto,
• Abstract summary: Slot-BERT scales object discovery seamlessly to long videos of unconstrained lengths.<n>We evaluate Slot-BERT on real-world surgical video datasets from abdominal, cholecystectomy, and thoracic procedures.
• Score: 9.224875902060083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object-centric slot attention is a powerful framework for unsupervised learning of structured and explainable representations that can support reasoning about objects and actions, including in surgical videos. While conventional object-centric methods for videos leverage recurrent processing to achieve efficiency, they often struggle with maintaining long-range temporal coherence required for long videos in surgical applications. On the other hand, fully parallel processing of entire videos enhances temporal consistency but introduces significant computational overhead, making it impractical for implementation on hardware in medical facilities. We present Slot-BERT, a bidirectional long-range model that learns object-centric representations in a latent space while ensuring robust temporal coherence. Slot-BERT scales object discovery seamlessly to long videos of unconstrained lengths. A novel slot contrastive loss further reduces redundancy and improves the representation disentanglement by enhancing slot orthogonality. We evaluate Slot-BERT on real-world surgical video datasets from abdominal, cholecystectomy, and thoracic procedures. Our method surpasses state-of-the-art object-centric approaches under unsupervised training achieving superior performance across diverse domains. We also demonstrate efficient zero-shot domain adaptation to data from diverse surgical specialties and databases.

Related papers

• Token Merging via Spatiotemporal Information Mining for Surgical Video Understanding [32.4892900455388]
  We propose video understanding token merging (STIM-TM) method, representing the first dedicated approach for surgical understanding tasks.<n>STIM-TM introduces a decoupled strategy that reduces token redundancy along temporal and spatial dimensions independently.<n> operating in a training-free manner, STIM-TM achieves significant efficiency with over $65$ GFLOPs reduction while preserving competitive accuracy across comprehensive surgical video tasks.
  arXiv  Detail & Related papers  (2025-09-28T06:24:57Z)
• SurgLLM: A Versatile Large Multimodal Model with Spatial Focus and Temporal Awareness for Surgical Video Understanding [75.00667948967848]
  The SurgLLM framework is a large multimodal model tailored for versatile surgical video understanding tasks.<n>To empower the spatial focus of surgical videos, we first devise Surgical Context-aware Multimodal Pretraining (Surg-Pretrain) for the video encoder of SurgLLM.<n>To incorporate surgical temporal knowledge into SurgLLM, we further propose Temporal-aware Multimodal Tuning (TM-Tuning) to enhance temporal reasoning with interleaved multimodal embeddings.
  arXiv  Detail & Related papers  (2025-08-30T04:36:41Z)
• Future Slot Prediction for Unsupervised Object Discovery in Surgical Video [10.984331138780682]
  Object-centric slot attention is an emerging paradigm for unsupervised learning of structured, interpretable object-centric representations.<n>Current approaches with an adaptive slot count perform well on images, but their performance on surgical videos is low.<n>We propose a dynamic temporal slot transformer (DTST) module that is trained both for temporal reasoning and for predicting the optimal future slot.
  arXiv  Detail & Related papers  (2025-07-02T16:52:16Z)
• Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance [50.486523249499115]
  Real-time video understanding is critical to guide procedures in minimally invasive surgery (MIS)<n>We propose Compress-to-Explore (C2E), a novel self-supervised framework to learn compact, informative representations from surgical videos.<n>C2E uses entropy-maximizing decoders to compress images while preserving clinically relevant details, improving encoder performance without labeled data.
  arXiv  Detail & Related papers  (2025-05-16T14:02:24Z)
• SASVi - Segment Any Surgical Video [2.330834737588252]
  We propose SASVi, a novel re-prompting mechanism based on a frame-wise Mask R-CNN Overseer model. This model automatically re-prompts the foundation model SAM2 when the scene constellation changes.
  arXiv  Detail & Related papers  (2025-02-12T00:29:41Z)
• Vivim: a Video Vision Mamba for Medical Video Segmentation [52.11785024350253]
  This paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for medical video segmentation tasks. Our Vivim can effectively compress the long-term representation into sequences at varying scales. Experiments on thyroid segmentation, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim.
  arXiv  Detail & Related papers  (2024-01-25T13:27:03Z)
• Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane [58.871015937204255]
  We introduce Fast Orthogonal Plane (plane) for the reconstruction of deformable tissues. We conceptualize surgical procedures as 4D volumes, and break them down into static and dynamic fields comprised of neural planes. This factorization iscretizes four-dimensional space, leading to a decreased memory usage and faster optimization.
  arXiv  Detail & Related papers  (2023-12-23T13:27:50Z)
• Surgical Temporal Action-aware Network with Sequence Regularization for Phase Recognition [28.52533700429284]
  We propose a Surgical Temporal Action-aware Network with sequence Regularization, named STAR-Net, to recognize surgical phases more accurately from input videos. MS-STA module integrates visual features with spatial and temporal knowledge of surgical actions at the cost of 2D networks. Our STAR-Net with MS-STA and DSR can exploit visual features of surgical actions with effective regularization, thereby leading to the superior performance of surgical phase recognition.
  arXiv  Detail & Related papers  (2023-11-21T13:43:16Z)
• GLSFormer : Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos [57.93194315839009]
  We propose a vision transformer-based approach to learn temporal features directly from sequence-level patches. We extensively evaluate our approach on two cataract surgery video datasets, Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-20T17:57:04Z)
• TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition [1.5237530964650965]
  We propose TUNeS, an efficient and simple temporal model that incorporates self-attention at the core of a convolutional U-Net structure. In our experiments, almost all temporal models performed better on top of feature extractors that were trained with longer temporal context.
  arXiv  Detail & Related papers  (2023-07-19T14:10:55Z)
• LoViT: Long Video Transformer for Surgical Phase Recognition [59.06812739441785]
  We present a two-stage method, called Long Video Transformer (LoViT) for fusing short- and long-term temporal information. Our approach outperforms state-of-the-art methods on the Cholec80 and AutoLaparo datasets consistently.
  arXiv  Detail & Related papers  (2023-05-15T20:06:14Z)
• Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video [53.14186293442669]
  We identify two important clues for surgical instrument perception, including local temporal dependency from adjacent frames and global semantic correlation in long-range duration. We propose a novel dual-memory network (DMNet) to relate both global and local-temporal knowledge. Our method largely outperforms the state-of-the-art works on segmentation accuracy while maintaining a real-time speed.
  arXiv  Detail & Related papers  (2021-09-28T10:10:14Z)
• Multi-frame Feature Aggregation for Real-time Instrument Segmentation in Endoscopic Video [11.100734994959419]
  We propose a novel Multi-frame Feature Aggregation (MFFA) module to aggregate video frame features temporally and spatially. We also develop a method that can randomly synthesize a surgical frame sequence from a single labeled frame to assist network training.
  arXiv  Detail & Related papers  (2020-11-17T16:27:27Z)
• Symmetric Dilated Convolution for Surgical Gesture Recognition [10.699258974625073]
  We propose a novel temporal convolutional architecture to automatically detect and segment surgical gestures. We devise our method with a symmetric dilation structure bridged by a self-attention module to encode and decode the long-term temporal patterns. We validate our approach on a fundamental robotic suturing task from the JIGSAWS dataset.
  arXiv  Detail & Related papers  (2020-07-13T13:34:48Z)
• Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery [1.1033115844630357]
  We investigate automatic surgical activity recognition in robot-assisted operations. We collect the first large-scale dataset including 400 full-length multi-perspective videos. We densely annotate the videos with 10 most recognized and clinically relevant classes of activities.
  arXiv  Detail & Related papers  (2020-06-29T16:30:31Z)
• LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition [67.86810761677403]
  We propose a novel active learning method for cost-effective surgical video analysis. Specifically, we propose a non-local recurrent convolutional network (NL-RCNet), which introduces non-local block to capture the long-range temporal dependency. We validate our approach on a large surgical video dataset (Cholec80) by performing surgical workflow recognition task.
  arXiv  Detail & Related papers  (2020-04-21T09:21:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.