Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot

AbstractData Matrix patterns imprinted as passive visual landmarks have shown to be a valid solution for the self-localization of Automated Guided Vehicles (AGVs) in shop floors. However, existing Data Matrix decoding applications take a long time to detect and segment the markers in the input image. Therefore, this paper proposes a pipeline where the detector is based on a real-time Deep Learning network and the decoder is a conventional method, i.e. the implementation in libdmtx. To do so, several types of Deep Neural Networks (DNNs) for object detection were studied, trained, compared, and assessed. The architectures range from region proposals (Faster R-CNN) to single-shot methods (SSD and YOLO). This study focused on performance and processing time to select the best Deep Learning (DL) model to carry out the detection of the visual markers. Additionally, a specific data set was created to evaluate those networks. This test set includes demanding situations, such as high illumination gradients in the same scene and Data Matrix markers positioned in skewed planes. The proposed approach outperformed the best known and most used Data Matrix decoder available in libraries like libdmtx.

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Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot

10.1101/2020.04.22.056382 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sukrit Gupta ◽

Yi Hao Chan ◽

Jagath C. Rajapakse ◽

Keyword(s):

Deep Neural Network ◽

Deep Neural Networks ◽

Brain Regions ◽

Single Shot ◽

Healthy Controls ◽

Brain Disorders ◽

Disease Biomarkers ◽

Functional Connectome ◽

Biomedical Computing ◽

Full Network

AbstractNeuroscientific knowledge points to the presence of redundancy in the correlations of brain’s functional activity. These redundancies can be removed to mitigate the problem of overfitting when deep neural network (DNN) models are used to classify neuroimaging datasets. We propose an algorithm that removes insignificant nodes of DNNs in a layerwise manner and then adds a subset of correlated features in a single shot. When performing experiments with functional MRI datasets for classifying patients from healthy controls, we were able to obtain simpler and more generalizable DNNs. The obtained DNNs maintained a similar performance as the full network with only around 2% of the initial trainable parameters. Further, we used the trained network to identify salient brain regions and connections from functional connectome for multiple brain disorders. The identified biomarkers were found to closely correspond to previously known disease biomarkers. The proposed methods have cross-modal applications in obtaining leaner DNNs that seem to fit the data better. The corresponding code is available at https://github.com/SCSE-Biomedical-Computing-Group/LEAN_CLIP.

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Deep Neural Networks for Determining the Parameters of Buildings from Single-Shot Satellite Imagery

Journal of Computer and Systems Sciences International ◽

10.1134/s106423072005007x ◽

2020 ◽

Vol 59 (5) ◽

pp. 755-767

Author(s):

A. N. Trekin ◽

V. Yu. Ignatiev ◽

P. Ya. Yakubovskii

Keyword(s):

Neural Networks ◽

Satellite Imagery ◽

Deep Neural Networks ◽

Single Shot

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SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

Sensors ◽

10.3390/s20164587 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4587 ◽

Cited By ~ 3

Author(s):

Ángel Morera ◽

Ángel Sánchez ◽

A. Belén Moreno ◽

Ángel D. Sappa ◽

José F. Vélez

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Semantic Segmentation ◽

The Other ◽

Single Shot ◽

Partial Occlusion ◽

True Positive ◽

Detection Problem ◽

Different Types ◽

Outdoor Advertisement

This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.

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