How to Visualize Filters and Feature Maps in #ConvolutionalNeuralNetworks
After completing this tutorial, you will know:
* How to develop a visualization for specific filters in a convolutional neural network.
* How to develop a visualization for specific feature maps in a convolutional neural network.
* How to systematically visualize feature maps for each block in a #deep convolutional neural network.
Link
đź” @DeepGravity
After completing this tutorial, you will know:
* How to develop a visualization for specific filters in a convolutional neural network.
* How to develop a visualization for specific feature maps in a convolutional neural network.
* How to systematically visualize feature maps for each block in a #deep convolutional neural network.
Link
đź” @DeepGravity
Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with #Deep Feature Maps
3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging #data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches object portions using an extended search. Experimental results confirm that the proposed method achieves 2.96 consistent tracking accuracy and 35.48 than the state-of-art methods.
Link
đź” @DeepGravity
3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging #data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches object portions using an extended search. Experimental results confirm that the proposed method achieves 2.96 consistent tracking accuracy and 35.48 than the state-of-art methods.
Link
đź” @DeepGravity
A #DeepLearning framework for #neuroscience
Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, #cognitive and motor tasks. Conversely, #ArtificialIntelligence attempts to design computational systems based on the tasks they will have to solve. In artificial #NeuralNetworks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of #deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.
Link
đź” @DeepGravity
Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, #cognitive and motor tasks. Conversely, #ArtificialIntelligence attempts to design computational systems based on the tasks they will have to solve. In artificial #NeuralNetworks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of #deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.
Link
đź” @DeepGravity
Nature Neuroscience
A deep learning framework for neuroscience
A deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation. Richards et al. argue that this inspires fruitful approaches to systems neuroscience.
Semantic Segmentation of Thigh Muscle using 2.5D #DeepLearning Network Trained with Limited Datasets
Purpose: We propose a 2.5D #deep learning #NeuralNetwork (#DLNN) to automatically classify thigh muscle into 11 classes and evaluate its classification accuracy over 2D and 3D DLNN when trained with limited datasets. Enables operator invariant quantitative assessment of the thigh muscle volume change with respect to the disease progression. Materials and methods: Retrospective datasets consist of 48 thigh volume (TV) cropped from CT DICOM images. Cropped volumes were aligned with femur axis and resample in 2 mm voxel-spacing. Proposed 2.5D DLNN consists of three 2D U-Net trained with axial, coronal and sagittal muscle slices respectively. A voting algorithm was used to combine the output of U-Nets to create final segmentation. 2.5D U-Net was trained on PC with 38 TV and the remaining 10 TV were used to evaluate segmentation accuracy of 10 classes within Thigh. The result segmentation of both left and right thigh were de-cropped to original CT volume space. Finally, segmentation accuracies were compared between proposed DLNN and 2D/3D U-Net. Results: Average segmentation DSC score accuracy of all classes with 2.5D U-Net as 91.18 mean DSC score for 2D U-Net was 3.3 DSC score of 3D U-Net was 5.7 same datasets. Conclusion: We achieved a faster computationally efficient and automatic segmentation of thigh muscle into 11 classes with reasonable accuracy. Enables quantitative evaluation of muscle atrophy with disease progression.
Link
đź” @DeepGravity
Purpose: We propose a 2.5D #deep learning #NeuralNetwork (#DLNN) to automatically classify thigh muscle into 11 classes and evaluate its classification accuracy over 2D and 3D DLNN when trained with limited datasets. Enables operator invariant quantitative assessment of the thigh muscle volume change with respect to the disease progression. Materials and methods: Retrospective datasets consist of 48 thigh volume (TV) cropped from CT DICOM images. Cropped volumes were aligned with femur axis and resample in 2 mm voxel-spacing. Proposed 2.5D DLNN consists of three 2D U-Net trained with axial, coronal and sagittal muscle slices respectively. A voting algorithm was used to combine the output of U-Nets to create final segmentation. 2.5D U-Net was trained on PC with 38 TV and the remaining 10 TV were used to evaluate segmentation accuracy of 10 classes within Thigh. The result segmentation of both left and right thigh were de-cropped to original CT volume space. Finally, segmentation accuracies were compared between proposed DLNN and 2D/3D U-Net. Results: Average segmentation DSC score accuracy of all classes with 2.5D U-Net as 91.18 mean DSC score for 2D U-Net was 3.3 DSC score of 3D U-Net was 5.7 same datasets. Conclusion: We achieved a faster computationally efficient and automatic segmentation of thigh muscle into 11 classes with reasonable accuracy. Enables quantitative evaluation of muscle atrophy with disease progression.
Link
đź” @DeepGravity
#DBSN: Measuring Uncertainty through #Bayesian Learning of #DeepNeuralNetwork Structures
Bayesian neural networks (BNNs) introduce uncertainty estimation to #deep networks by performing Bayesian inference on network weights. However, such models bring the challenges of inference, and further BNNs with weight uncertainty rarely achieve superior performance to standard models. In this paper, we investigate a new line of Bayesian deep learning by performing Bayesian reasoning on the structure of deep neural networks. Drawing inspiration from the neural architecture search, we define the network structure as gating weights on the redundant operations between computational nodes, and apply stochastic variational inference techniques to learn the structure distributions of networks. Empirically, the proposed method substantially surpasses the advanced deep neural networks across a range of classification and segmentation tasks. More importantly, our approach also preserves benefits of Bayesian principles, producing improved uncertainty estimation than the strong baselines including MC dropout and variational #BNNs algorithms (e.g. noisy EK-FAC).
Link
đź” @DeepGravity
Bayesian neural networks (BNNs) introduce uncertainty estimation to #deep networks by performing Bayesian inference on network weights. However, such models bring the challenges of inference, and further BNNs with weight uncertainty rarely achieve superior performance to standard models. In this paper, we investigate a new line of Bayesian deep learning by performing Bayesian reasoning on the structure of deep neural networks. Drawing inspiration from the neural architecture search, we define the network structure as gating weights on the redundant operations between computational nodes, and apply stochastic variational inference techniques to learn the structure distributions of networks. Empirically, the proposed method substantially surpasses the advanced deep neural networks across a range of classification and segmentation tasks. More importantly, our approach also preserves benefits of Bayesian principles, producing improved uncertainty estimation than the strong baselines including MC dropout and variational #BNNs algorithms (e.g. noisy EK-FAC).
Link
đź” @DeepGravity
#Internship Opportunities: Researcher #ReinforcementLearning for #Game Intelligence
Cambridge, Cambridgeshire, United Kingdom, #Microsoft #AI and Research
This is an exceptional opportunity to drive ambitious research while collaborating with a diverse team. Key research challenges we are currently tackling include, but are not limited to, robustness and generalization in (#deep) #RL, multi-agent RL, sample-efficiency and scalability of RL algorithms. The focus and scope of internship projects considers the team’s direction as well as successful candidates’ experience and research interests.
Link
#Job
đź” @DeepGravity
Cambridge, Cambridgeshire, United Kingdom, #Microsoft #AI and Research
This is an exceptional opportunity to drive ambitious research while collaborating with a diverse team. Key research challenges we are currently tackling include, but are not limited to, robustness and generalization in (#deep) #RL, multi-agent RL, sample-efficiency and scalability of RL algorithms. The focus and scope of internship projects considers the team’s direction as well as successful candidates’ experience and research interests.
Link
#Job
đź” @DeepGravity
Microsoft
Internship Opportunities: Researcher Reinforcement Learning for Game Intelligence in Cambridge, Cambridgeshire, United Kingdom…
Apply for Internship Opportunities: Researcher Reinforcement Learning for Game Intelligence job with Microsoft in Cambridge, Cambridgeshire, United Kingdom. Research at Microsoft