Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning

• URL: http://arxiv.org/abs/2012.09237v3
• Date: Tue, 6 Apr 2021 13:52:14 GMT
• Title: Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning
• Authors: Biagio Brattoli, Uta Buechler, Michael Dorkenwald, Philipp Reiser, Linard Filli, Fritjof Helmchen, Anna-Sophia Wahl, Bjoern Ommer
• Abstract summary: Motor behaviour analysis provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. We introduce unsupervised behaviour analysis and magnification (uBAM), an automatic deep learning algorithm for analysing behaviour by discovering and magnifying deviations. A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement.
• Score: 5.101123537955207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motor behaviour analysis is essential to biomedical research and clinical diagnostics as it provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. State-of-the-art instrumented movement analysis is time- and cost-intensive, since it requires placing physical or virtual markers. Besides the effort required for marking keypoints or annotations necessary for training or finetuning a detector, users need to know the interesting behaviour beforehand to provide meaningful keypoints. We introduce unsupervised behaviour analysis and magnification (uBAM), an automatic deep learning algorithm for analysing behaviour by discovering and magnifying deviations. A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement. Besides discovering and quantifying deviations in behaviour, we also propose a generative model for visually magnifying subtle behaviour differences directly in a video without requiring a detour via keypoints or annotations. Essential for this magnification of deviations even across different individuals is a disentangling of appearance and behaviour. Evaluations on rodents and human patients with neurological diseases demonstrate the wide applicability of our approach. Moreover, combining optogenetic stimulation with our unsupervised behaviour analysis shows its suitability as a non-invasive diagnostic tool correlating function to brain plasticity.

Related papers

• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data [0.0]
  This study evaluates the performance of various supervised machine learning models in analyzing highly correlated neural signaling data from the Adolescent Brain Cognitive Development (ABCD) Study. We simulated a dataset to mimic the correlation structures commonly found in neuroimaging data and evaluated logistic regression, elastic networks, random forests, and XGBoost on their ability to handle multicollinearity and accurately identify predictive features.
  arXiv  Detail & Related papers  (2024-05-14T23:43:34Z)
• Investigating Human-Identifiable Features Hidden in Adversarial Perturbations [54.39726653562144]
  Our study explores up to five attack algorithms across three datasets. We identify human-identifiable features in adversarial perturbations. Using pixel-level annotations, we extract such features and demonstrate their ability to compromise target models.
  arXiv  Detail & Related papers  (2023-09-28T22:31:29Z)
• A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images [71.26717896083433]
  Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging.
  arXiv  Detail & Related papers  (2023-09-05T19:45:09Z)
• Deep-seeded Clustering for Unsupervised Valence-Arousal Emotion Recognition from Physiological Signals [1.5695847325697105]
  This article proposes an unsupervised deep cluster framework for emotion recognition from physiological and psychological data. Tests on the open benchmark data set WESAD show that deep k-means and deep c-means distinguish the four quadrants of Russell's circumplex model of affect with an overall accuracy of 87%.
  arXiv  Detail & Related papers  (2023-08-17T14:37:35Z)
• Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism [20.09315869162054]
  This paper focuses on automatically detecting stereotypical behaviours with computer vision techniques. We propose a Dual Stream deep model for Stereotypical Behaviours Detection, DS-SBD, based on the temporal trajectory of human poses and the repetition patterns of human actions.
  arXiv  Detail & Related papers  (2023-02-27T13:24:08Z)
• Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements [0.0]
  Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision. Recent advances in deep learning seem to decrease this similarity. We investigate a purely data-driven approach to obtain useful models.
  arXiv  Detail & Related papers  (2022-06-21T17:59:23Z)
• Computational behavior recognition in child and adolescent psychiatry: A statistical and machine learning analysis plan [3.975358343371988]
  We aim to automate coding of human behavior for use in psychotherapy and research with the help of artificial intelligence (AI) tools. Videos of a gold-standard semi-structured diagnostic interview of 25 youth with obsessive-compulsive disorder (OCD) and 12 youth without a psychiatric diagnosis (no-OCD) will be analyzed.
  arXiv  Detail & Related papers  (2022-05-11T19:12:15Z)
• Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations [60.47807856873544]
  A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies. This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
  arXiv  Detail & Related papers  (2021-11-29T15:27:51Z)
• Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification [101.49122450005869]
  We present a counterfactual attention learning method to learn more effective attention based on causal inference. Specifically, we analyze the effect of the learned visual attention on network prediction. We evaluate our method on a wide range of fine-grained recognition tasks.
  arXiv  Detail & Related papers  (2021-08-19T14:53:40Z)
• Muti-view Mouse Social Behaviour Recognition with Deep Graphical Model [124.26611454540813]
  Social behaviour analysis of mice is an invaluable tool to assess therapeutic efficacy of neurodegenerative diseases. Because of the potential to create rich descriptions of mouse social behaviors, the use of multi-view video recordings for rodent observations is increasingly receiving much attention. We propose a novel multiview latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures.
  arXiv  Detail & Related papers  (2020-11-04T18:09:58Z)

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.