Convolutional neural network for bio-medical image segmentation with hardware acceleration

Vardhana, M; Arunkumar, N.; Lasrado, Sunitha; Abdulhay, Enas; Ramírez-González, Gustavo

doi:10.1016/j.cogsys.2018.03.005

Cited by 120 publications

(37 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, with the popularization of smart devices, the application scenario of deep neural network applications has grown far beyond the high-performance platforms in their infancy. From computer vision (Redmon et al 2016) to image processing (Vardhana et al 2018), from audio analysis (Sehgal and Kehtarnavaz 2018) to natural language processing (Goldberg 2017), various edge portable and lowpower embedded platforms represented by smartphones have gradually become the main processing platforms for deep learning applications. The efficient and timely processing of deep learning applications on these embedded platforms has gradually become an increasingly important optimization design problem in deep learning research and practice.…”

Section: Neural Network Backgroundmentioning

confidence: 99%

To cloud or not to cloud: an on-line scheduler for dynamic privacy-protection of deep learning workload on edge devices

Tang

Wang

et al. 2020

CCF Trans. HPC

View full text Add to dashboard Cite

Recently deep learning applications are thriving on edge and mobile computing scenarios, due to the concerns of latency constraints, data security and privacy, and other considerations. However, because of the limitation of power delivery, battery lifetime and computation resource, offering real-time neural network inference ability has to resort to the specialized energy-efficient architecture, and sometimes the coordination between the edge devices and the powerful cloud or fog facilities. This work investigates a realistic scenario when an on-line scheduler is needed to meet the requirement of latency even when the edge computing resources and communication speed are dynamically fluctuating, while protecting the privacy of users as well. It also leverages the approximate computing feature of neural networks and actively trade-off excessive neural network propagation paths for latency guarantee even when local resource provision is unstable. Combining neural network approximation and dynamic scheduling, the real-time deep learning system could adapt to different requirements of latency/ accuracy and the resource fluctuation of mobile-cloud applications. Experimental results also demonstrate that the proposed scheduler significantly improves the energy efficiency of real-time neural networks on edge devices.

show abstract

Section: Neural Network Backgroundmentioning

confidence: 99%

To cloud or not to cloud: an on-line scheduler for dynamic privacy-protection of deep learning workload on edge devices

Tang

Wang

et al. 2020

CCF Trans. HPC

View full text Add to dashboard Cite

show abstract

“…When real data are not sufficient, it is possible to generate augmented data by data augmentation techniques such as reflection, translation, rotation, etc. Using DNNs for image segmentation in computed topography (CT) [1,2], magnetic resonance (MR) [3][4][5] or X-ray [6,7] images has become standard, while promising results are being obtained with DL in microscopy [8][9][10][11][12][13] and electron microscopy [14][15][16][17][18]. Furthermore, DNNs are successfully implemented for nucleus segmentation [19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modelling of Ground Truth Image for 3D Microscopic Objects Using Cascade of Convolutional Neural Networks Optimized with Parameters’ Combinations Generators

et al. 2020

View full text Add to dashboard Cite

Mathematical modelling to compute ground truth from 3D images is an area of research that can strongly benefit from machine learning methods. Deep neural networks (DNNs) are state-of-the-art methods design for solving these kinds of difficulties. Convolutional neural networks (CNNs), as one class of DNNs, can overcome special requirements of quantitative analysis especially when image segmentation is needed. This article presents a system that uses a cascade of CNNs with symmetric blocks of layers in chain, dedicated to 3D image segmentation from microscopic images of 3D nuclei. The system is designed through eight experiments that differ in following aspects: number of training slices and 3D samples for training, usage of pre-trained CNNs and number of slices and 3D samples for validation. CNNs parameters are optimized using linear, brute force, and random combinatorics, followed by voter and median operations. Data augmentation techniques such as reflection, translation and rotation are used in order to produce sufficient training set for CNNs. Optimal CNN parameters are reached by defining 11 standard and two proposed metrics. Finally, benchmarking demonstrates that CNNs improve segmentation accuracy, reliability and increased annotation accuracy, confirming the relevance of CNNs to generate high-throughput mathematical ground truth 3D images.

show abstract

“…CNNs have receptive fields with weight attributes called as convolutional units which are shifted step by step across a 2-dimensional array of input values [22]. This structure has recently successfully applied to various research areas from image recognition to bio-medical image segmentation [6,9,13,16,19,25,27]. CNNs have a linear mathematical operation of convolution and parameters of the convolution layer contain learnable filters or kernels.…”

Section: Introductionmentioning

confidence: 99%

Regp: A New Pooling Algorithm for Deep Convolutional Neural Networks

Yıldırım¹,

Baloğlu²

2019

NNW

View full text Add to dashboard Cite

In this paper, we propose a new pooling method for deep convolutional neural networks. Previously introduced pooling methods either have very simple assumptions or they depend on stochastic events. Different from those methods, RegP pooling intensely investigates the input data. The main idea of this approach is finding the most distinguishing parts in regions of the input by investigating neighborhood regions to construct the pooled representation. RegP pooling improves the efficiency of the learning process, which is clearly visible in the experimental results. Further, the proposed pooling method outperformed other widely used hand-crafted pooling methods on several benchmark datasets.

show abstract

Convolutional neural network for bio-medical image segmentation with hardware acceleration

Cited by 120 publications

References 1 publication

To cloud or not to cloud: an on-line scheduler for dynamic privacy-protection of deep learning workload on edge devices

To cloud or not to cloud: an on-line scheduler for dynamic privacy-protection of deep learning workload on edge devices

Mathematical Modelling of Ground Truth Image for 3D Microscopic Objects Using Cascade of Convolutional Neural Networks Optimized with Parameters’ Combinations Generators

Regp: A New Pooling Algorithm for Deep Convolutional Neural Networks

Contact Info

Product

Resources

About