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Bailly R, Malfante M, Allier C, Paviolo C, Ghenim L, Padmanabhan K, Bardin S, Mars J. Detecting abnormal cell behaviors from dry mass time series. Sci Rep 2024; 14:7053. [PMID: 38528035 DOI: 10.1038/s41598-024-57684-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 03/20/2024] [Indexed: 03/27/2024] Open
Abstract
The prediction of pathological changes on single cell behaviour is a challenging task for deep learning models. Indeed, in self-supervised learning methods, no prior labels are used for the training and all of the information for event predictions are extracted from the data themselves. We present here a novel self-supervised learning model for the detection of anomalies in a given cell population, StArDusTS. Cells are monitored over time, and analysed to extract time-series of dry mass values. We assessed its performances on different cell lines, showing a precision of 96% in the automatic detection of anomalies. Additionally, anomaly detection was also associated with cell measurement errors inherent to the acquisition or analysis pipelines, leading to an improvement of the upstream methods for feature extraction. Our results pave the way to novel architectures for the continuous monitoring of cell cultures in applied research or bioproduction applications, and for the prediction of pathological cellular changes.
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Affiliation(s)
- Romain Bailly
- Univ. Grenoble Alpes, CEA, List, F-38000, Grenoble, France
- Univ. Grenoble Alpes, CNRS, Grenoble-INP, GIPSA-Lab, 38000, Grenoble, France
| | | | - Cédric Allier
- Univ. Grenoble Alpes, CEA, Leti, F-38000, Grenoble, France
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Chiara Paviolo
- Univ. Grenoble Alpes, CEA, Leti, F-38000, Grenoble, France
| | - Lamya Ghenim
- Univ. Grenoble Alpes, INSERM, CEA-IRIG, BGE, Biomics, F-38000, Grenoble, France
| | - Kiran Padmanabhan
- Institut de Génomique Fonctionnelle de Lyon, Univ. Lyon, CNRS/ENS, UMR 5242, Lyon, France
| | - Sabine Bardin
- Institut Curie, PSL Research University, CNRS, UMR 144, Molecular Mechanisms of Intracellular Transport, F-75005, Paris, France
| | - Jérôme Mars
- Univ. Grenoble Alpes, CNRS, Grenoble-INP, GIPSA-Lab, 38000, Grenoble, France
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Defauw N, Malfante M, Antoni O, Rakotovao T, Lesecq S. Vehicle Detection on Occupancy Grid Maps: Comparison of Five Detectors Regarding Real-Time Performance. Sensors (Basel) 2023; 23:1613. [PMID: 36772653 PMCID: PMC9921887 DOI: 10.3390/s23031613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/25/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Occupancy grid maps are widely used as an environment model that allows the fusion of different range sensor technologies in real-time for robotics applications. In an autonomous vehicle setting, occupancy grid maps are especially useful for their ability to accurately represent the position of surrounding obstacles while being robust to discrepancies between the fused sensors through the use of occupancy probabilities representing uncertainty. In this article, we propose to evaluate the applicability of real-time vehicle detection on occupancy grid maps. State of the art detectors in sensor-specific domains such as YOLOv2/YOLOv3 for images or PIXOR for LiDAR point clouds are modified to use occupancy grid maps as input and produce oriented bounding boxes enclosing vehicles as output. The five proposed detectors are trained on the Waymo Open automotive dataset and compared regarding the quality of their detections measured in terms of Average Precision (AP) and their real-time capabilities measured in Frames Per Second (FPS). Of the five detectors presented, one inspired from the PIXOR backbone reaches the highest AP0.7 of 0.82 and runs at 20 FPS. Comparatively, two other proposed detectors inspired from YOLOv2 achieve an almost as good, with a AP0.7 of 0.79 while running at 91 FPS. These results validate the feasibility of real-time vehicle detection on occupancy grids.
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Affiliation(s)
- Nils Defauw
- Univ. Grenoble Alpes, CEA, List, F-38000 Grenoble, France
| | | | - Olivier Antoni
- Univ. Grenoble Alpes, CEA, List, F-38000 Grenoble, France
| | | | - Suzanne Lesecq
- Univ. Grenoble Alpes, CEA, Leti, F-38000 Grenoble, France
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Abstract
The work presented in this paper focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. Specifically, it focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. To this end, various indicators can be used to monitor marine areas such as both the geographical and temporal evolution of fish populations. A discriminative model is built using supervised machine learning (random-forest and support-vector machines). Each acquisition is represented in a feature space, in which the patterns belonging to different semantic classes are as separable as possible. The set of features proposed for describing the acquisitions come from an extensive state of the art in various domains in which classification of acoustic signals is performed, including speech, music, and environmental acoustics. Furthermore, this study proposes to extract features from three representations of the data (time, frequency, and cepstral domains). The proposed classification scheme is tested on real fish sounds recorded on several areas, and achieves 96.9% correct classification compared to 72.5% when using reference state of the art features as descriptors. The classification scheme is also validated on continuous underwater recordings, thereby illustrating that it can be used to both detect and classify fish sounds in operational scenarios.
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Affiliation(s)
- Marielle Malfante
- Institute of Engineering University Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, 38000 Grenoble, France
| | - Jérôme I Mars
- Institute of Engineering University Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, 38000 Grenoble, France
| | - Mauro Dalla Mura
- Institute of Engineering University Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, 38000 Grenoble, France
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