Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis

• URL: http://arxiv.org/abs/2207.07894v1
• Date: Sat, 16 Jul 2022 10:32:27 GMT
• Title: Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis
• Authors: Muhammad Abdullah Jamal, Omid Mohareri
• Abstract summary: We propose a novel way to fuse the multi-modal data for a single video frame or image. We treat the multi-modal data as different views to train the model in an unsupervised manner via clustering. Results show the superior performance of our approach on surgical video activity recognition and semantic segmentation.
• Score: 4.866110274299399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven approaches to assist operating room (OR) workflow analysis depend on large curated datasets that are time consuming and expensive to collect. On the other hand, we see a recent paradigm shift from supervised learning to self-supervised and/or unsupervised learning approaches that can learn representations from unlabeled datasets. In this paper, we leverage the unlabeled data captured in robotic surgery ORs and propose a novel way to fuse the multi-modal data for a single video frame or image. Instead of producing different augmentations (or 'views') of the same image or video frame which is a common practice in self-supervised learning, we treat the multi-modal data as different views to train the model in an unsupervised manner via clustering. We compared our method with other state of the art methods and results show the superior performance of our approach on surgical video activity recognition and semantic segmentation.

Related papers

• Unsupervised Meta-Learning via In-Context Learning [3.4165401459803335]
  We propose a novel approach to unsupervised meta-learning that leverages the generalization abilities of in-supervised learning. Our method reframes meta-learning as a sequence modeling problem, enabling the transformer encoder to learn task context from support images.
  arXiv  Detail & Related papers  (2024-05-25T08:29:46Z)
• Efficient Surgical Tool Recognition via HMM-Stabilized Deep Learning [25.146476653453227]
  We propose an HMM-stabilized deep learning method for tool presence detection. A range of experiments confirm that the proposed approaches achieve better performance with lower training and running costs. These results suggest that popular deep learning approaches with over-complicated model structures may suffer from inefficient utilization of data.
  arXiv  Detail & Related papers  (2024-04-07T15:27:35Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data [13.68491474904529]
  We propose Text-enhanced Visual Deep InfoMax (TVDIM) to learn better visual representations. Our core idea of self-supervised learning is to maximize the mutual information between features extracted from multiple views. TVDIM significantly outperforms previous visual self-supervised methods when processing the same set of images.
  arXiv  Detail & Related papers  (2021-06-03T12:36:01Z)
• CoCon: Cooperative-Contrastive Learning [52.342936645996765]
  Self-supervised visual representation learning is key for efficient video analysis. Recent success in learning image representations suggests contrastive learning is a promising framework to tackle this challenge. We introduce a cooperative variant of contrastive learning to utilize complementary information across views.
  arXiv  Detail & Related papers  (2021-04-30T05:46:02Z)
• Multimodal Contrastive Training for Visual Representation Learning [45.94662252627284]
  We develop an approach to learning visual representations that embraces multimodal data. Our method exploits intrinsic data properties within each modality and semantic information from cross-modal correlation simultaneously. By including multimodal training in a unified framework, our method can learn more powerful and generic visual features.
  arXiv  Detail & Related papers  (2021-04-26T19:23:36Z)
• Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos [69.61522804742427]
  This paper proposes a self-supervised training framework that learns a common multimodal embedding space. We extend the concept of instance-level contrastive learning with a multimodal clustering step to capture semantic similarities across modalities. The resulting embedding space enables retrieval of samples across all modalities, even from unseen datasets and different domains.
  arXiv  Detail & Related papers  (2021-04-26T15:55:01Z)
• Multiview Pseudo-Labeling for Semi-supervised Learning from Video [102.36355560553402]
  We present a novel framework that uses complementary views in the form of appearance and motion information for semi-supervised learning in video. Our method capitalizes on multiple views, but it nonetheless trains a model that is shared across appearance and motion input. On multiple video recognition datasets, our method substantially outperforms its supervised counterpart, and compares favorably to previous work on standard benchmarks in self-supervised video representation learning.
  arXiv  Detail & Related papers  (2021-04-01T17:59:48Z)
• Multi-Pretext Attention Network for Few-shot Learning with Self-supervision [37.6064643502453]
  We propose a novel augmentation-free method for self-supervised learning, which does not rely on any auxiliary sample. Besides, we propose Multi-pretext Attention Network (MAN), which exploits a specific attention mechanism to combine the traditional augmentation-relied methods and our GC. We evaluate our MAN extensively on miniImageNet and tieredImageNet datasets and the results demonstrate that the proposed method outperforms the state-of-the-art (SOTA) relevant methods.
  arXiv  Detail & Related papers  (2021-03-10T10:48:37Z)
• Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery [84.73764603474413]
  We propose a novel online approach of multi-modal graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information. The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset.
  arXiv  Detail & Related papers  (2020-11-03T11:00:10Z)
• Self-supervised Video Object Segmentation [76.83567326586162]
  The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a.k.a. dense tracking) We make the following contributions: (i) we propose to improve the existing self-supervised approach, with a simple, yet more effective memory mechanism for long-term correspondence matching; (ii) by augmenting the self-supervised approach with an online adaptation module, our method successfully alleviates tracker drifts caused by spatial-temporal discontinuity; (iv) we demonstrate state-of-the-art results among the self-supervised approaches on DAVIS-2017 and YouTube
  arXiv  Detail & Related papers  (2020-06-22T17:55:59Z)

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.