SEDMamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in Robot-Assisted Surgery

• URL: http://arxiv.org/abs/2406.15920v1
• Date: Sat, 22 Jun 2024 19:20:35 GMT
• Title: SEDMamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in Robot-Assisted Surgery
• Authors: Jialang Xu, Nazir Sirajudeen, Matthew Boal, Nader Francis, Danail Stoyanov, Evangelos Mazomenos,
• Abstract summary: We propose a novel hierarchical model named SEDMamba, which incorporates the selective state space model (SSM) into surgical error detection. SEDMamba enhances selective SSM with bottleneck mechanism and fine-to-coarse temporal fusion (FCTF) to detect and temporally localize surgical errors in long videos. FCTF utilizes multiple dilated 1D convolutional layers to merge temporal information across diverse scale ranges, accommodating errors of varying durations.
• Score: 7.863539113283565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated detection of surgical errors can improve robotic-assisted surgery. Despite promising progress, existing methods still face challenges in capturing rich temporal context to establish long-term dependencies while maintaining computational efficiency. In this paper, we propose a novel hierarchical model named SEDMamba, which incorporates the selective state space model (SSM) into surgical error detection, facilitating efficient long sequence modelling with linear complexity. SEDMamba enhances selective SSM with bottleneck mechanism and fine-to-coarse temporal fusion (FCTF) to detect and temporally localize surgical errors in long videos. The bottleneck mechanism compresses and restores features within their spatial dimension, thereby reducing computational complexity. FCTF utilizes multiple dilated 1D convolutional layers to merge temporal information across diverse scale ranges, accommodating errors of varying durations. Besides, we deploy an established observational clinical human reliability assessment tool (OCHRA) to annotate the errors of suturing tasks in an open-source radical prostatectomy dataset (SAR-RARP50), constructing the first frame-level in-vivo surgical error detection dataset to support error detection in real-world scenarios. Experimental results demonstrate that our SEDMamba outperforms state-of-the-art methods with at least 1.82% AUC and 3.80% AP performance gain with significantly reduced computational complexity.

Related papers

• Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection [1.0358639819750703]
  In unsupervised anomaly detection (UAD) research, it is necessary to develop a computationally efficient and scalable solution. We revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. We propose Feature Attenuation of Defective Representation (FADeR) that only employs two layers which attenuates feature information of anomaly reconstruction.
  arXiv  Detail & Related papers  (2024-07-05T15:44:53Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• Spatial-temporal Memories Enhanced Graph Autoencoder for Anomaly Detection in Dynamic Graphs [52.956235109354175]
  Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. We introduce a novel Spatial-Temporal memories-enhanced graph autoencoder (STRIPE) STRIPE has demonstrated a superior capability to discern anomalies by effectively leveraging the distinct spatial and temporal dynamics of dynamic graphs.
  arXiv  Detail & Related papers  (2024-03-14T02:26:10Z)
• Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps [1.5501208213584152]
  Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder impacting a large aging population. We propose an efficient early-late fusion (ELF) approach, which leverages a convolutional neural network for automated feature extraction and random forests. To tackle the challenge of detecting subtle changes in brain volume, we transform images into the Jacobian domain (JD)
  arXiv  Detail & Related papers  (2023-10-25T19:02:57Z)
• 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)
• 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)
• ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery [14.47768738295518]
  Learning of the task-oriented attention while tracking instrument holds vast potential in image-guided robotic surgery. We propose an end-to-end Multi-Task Learning (ST-MTL) model with a shared encoder and Sink-temporal decoders for the real-time surgical instrument segmentation and task-oriented saliency detection. We tackle the problem with a novel asynchronous-temporal optimization technique by calculating independent gradients for each decoder. Compared to the state-of-the-art segmentation and saliency methods, our model most outperforms the evaluation metrics and produces an outstanding performance in challenge
  arXiv  Detail & Related papers  (2021-12-10T15:20:27Z)
• Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction [4.033624665609417]
  We propose MemAAE, a novel unsupervised anomaly detection method for time-series. By jointly training two complementary proxy tasks, reconstruction and prediction, we show that detecting anomalies via multiple tasks obtains superior performance. MemAAE achieves an overall F1 score of 0.90 on four public datasets, significantly outperforming the best baseline by 0.02.
  arXiv  Detail & Related papers  (2021-10-15T18:29:05Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)
• AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery [23.33984309289549]
  Training a real-time robotic system for the detection and segmentation of high-resolution images provides a challenging problem with the limited computational resource. We develop a novel end-to-end trainable real-time Multi-Task Learning model with weight-shared encoder and task-aware detection and segmentation decoders. Our model significantly outperforms state-of-the-art segmentation and detection models, including best-performed models in the challenge.
  arXiv  Detail & Related papers  (2020-03-10T14:24:51Z)

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.