scholarly journals Convolving Pre-Trained Convolutional Neural Networks at Various Magnifications to Extract Diagnostic Features for Digital Pathology

2018 ◽  
Author(s):  
John-William Sidhom ◽  
Alexander S. Baras
Keyword(s):  
Deep Learning ◽  
Graphics Processing Units ◽  
Visual Recognition ◽  
Digital Pathology ◽  
Diagnostic Features ◽  
High Level ◽  
Graphics Processing ◽  
Computational Analyses ◽  
Radiology And Pathology

ABSTRACTDeep learning is an area of artificial intelligence that has received much attention in the past few years due to both an increase in computational power with the increased use of graphics processing units (GPU’s) for computational analyses and the performance of these class of algorithms on visual recognition tasks. They have found utility in applications ranging from image search to facial recognition for security and social media purposes. Their continued success has propelled their use across many new domains including the medical field, in areas of radiology and pathology in particular, as these fields are thought to be driven by visual recognition tasks. In this paper, we present an application of deep learning, termed ‘transfer learning’, using ResNet50, a pre-trained convolutional neural network (CNN) to act as a ‘feature-detector’ at various magnifications to identify low and high level features in digital pathology images of various breast lesions for the purpose of classifying them correctly into the labels of normal, benign, in-situ, or invasive carcinoma as provided in the ICIAR 2018 Breast Cancer Histology Challenge (BACH).

scholarly journals The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

2020 ◽  
Vol 12 (18) ◽  
pp. 3020
Author(s):  
Piotr Szymak ◽  
Paweł Piskur ◽  
Krzysztof Naus
Keyword(s):  
Deep Learning ◽  
World War Ii ◽  
Graphics Processing Units ◽  
Object Classification ◽  
Underwater Vehicles ◽  
Training Methods ◽  
Underwater Video ◽  
Deep Learning Neural Network ◽  
Graphics Processing

Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment.

Deep Learning With PyTorch

2020 ◽  
pp. 61-95
Author(s):  
Anmol Chaudhary ◽  
Kuldeep Singh Chouhan ◽  
Jyoti Gajrani ◽  
Bhavna Sharma
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Machine Translation ◽  
Exponential Growth ◽  
Graphics Processing Units ◽  
New Technologies ◽  
The Internet ◽  
Computational Power ◽  
Practical Applications ◽  
Graphics Processing

In the last decade, deep learning has seen exponential growth due to rise in computational power as a result of graphics processing units (GPUs) and a large amount of data due to the democratization of the internet and smartphones. This chapter aims to throw light on both the theoretical aspects of deep learning and its practical aspects using PyTorch. The chapter primarily discusses new technologies using deep learning and PyTorch in detail. The chapter discusses the advantages of using PyTorch compared to other deep learning libraries. The chapter discusses some of the practical applications like image classification and machine translation. The chapter also discusses the various frameworks built with the help of PyTorch. PyTorch consists of various models that increases its flexibility and accessibility to a greater extent. As a result, many frameworks built on top of PyTorch are discussed in this chapter. The authors believe that this chapter will help readers in getting a better understanding of deep learning making neural networks using PyTorch.

scholarly journals Detection of COVID-19 from CT scan images: A spiking neural network-based approach

2021 ◽  
Author(s):  
Avishek Garain ◽  
Arpan Basu ◽  
Fabio Giampaolo ◽  
Juan D. Velasquez ◽  
Ram Sarkar
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Graphics Processing Units ◽  
Early Stage ◽  
Medical Image Analysis ◽  
Global Pandemic ◽  
Radiological Images ◽  
Chest X Ray ◽  
Computer Based ◽  
Graphics Processing

AbstractThe outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.

scholarly journals Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning

2017 ◽  
Author(s):  
Haotian Teng ◽  
Minh Duc Cao ◽  
Michael B. Hall ◽  
Tania Duarte ◽  
Sheng Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Graphics Processing Units ◽  
State Of The Art ◽  
Electrical Signal ◽  
Desktop Computer ◽  
Small Set ◽  
Unseen Species ◽  
Graphics Processing ◽  
Deep Learning Model

ABSTRACTSequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling: directly translating the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4000 reads, we show that our model provides state-of-the-art basecalling accuracy even on previously unseen species. Chiron achieves basecalling speeds of over 2000 bases per second using desktop computer graphics processing units.

scholarly journals A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study

10.2196/17037 ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. e17037 ◽  
Author(s):  
Eunjoo Jeon ◽  
Kyusam Oh ◽  
Soonhwan Kwon ◽  
HyeongGwan Son ◽  
Yongkeun Yun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Graphics Processing Units ◽  
Model Complexity ◽  
Massachusetts Institute Of Technology ◽  
Baseline Model ◽  
Accurate Analysis ◽  
Central Processing ◽  
Beat Classification ◽  
Institute Of Technology ◽  
Graphics Processing

Background Electrocardiographic (ECG) monitors have been widely used for diagnosing cardiac arrhythmias for decades. However, accurate analysis of ECG signals is difficult and time-consuming work because large amounts of beats need to be inspected. In order to enhance ECG beat classification, machine learning and deep learning methods have been studied. However, existing studies have limitations in model rigidity, model complexity, and inference speed. Objective To classify ECG beats effectively and efficiently, we propose a baseline model with recurrent neural networks (RNNs). Furthermore, we also propose a lightweight model with fused RNN for speeding up the prediction time on central processing units (CPUs). Methods We used 48 ECGs from the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database, and 76 ECGs were collected with S-Patch devices developed by Samsung SDS. We developed both baseline and lightweight models on the MXNet framework. We trained both models on graphics processing units and measured both models’ inference times on CPUs. Results Our models achieved overall beat classification accuracies of 99.72% for the baseline model with RNN and 99.80% for the lightweight model with fused RNN. Moreover, our lightweight model reduced the inference time on CPUs without any loss of accuracy. The inference time for the lightweight model for 24-hour ECGs was 3 minutes, which is 5 times faster than the baseline model. Conclusions Both our baseline and lightweight models achieved cardiologist-level accuracies. Furthermore, our lightweight model is competitive on CPU-based wearable hardware.

scholarly journals Bringing heterogeneity to the CMS software framework

2020 ◽  
Vol 245 ◽  
pp. 05009
Author(s):  
Andrea Bocci ◽  
David Dagenhart ◽  
Vincenzo Innocente ◽  
Christopher Jones ◽  
Matti Kortelainen ◽  
...  
Keyword(s):  
Data Processing ◽  
Graphics Processing Units ◽  
Building Blocks ◽  
Current Data ◽  
Level Trigger ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
High Level ◽  
Graphics Processing ◽  
Leadership Class

The advent of computing resources with co-processors, for example Graphics Processing Units (GPU) or Field-Programmable Gate Arrays (FPGA), for use cases like the CMS High-Level Trigger (HLT) or data processing at leadership-class supercomputers imposes challenges for the current data processing frameworks. These challenges include developing a model for algorithms to offload their computations on the co-processors as well as keeping the traditional CPU busy doing other work. The CMS data processing framework, CMSSW, implements multithreading using the Intel Threading Building Blocks (TBB) library, that utilizes tasks as concurrent units of work. In this paper we will discuss a generic mechanism to interact effectively with non-CPU resources that has been implemented in CMSSW. In addition, configuring such a heterogeneous system is challenging. In CMSSW an application is configured with a configuration file written in the Python language. The algorithm types are part of the configuration. The challenge therefore is to unify the CPU and co-processor settings while allowing their implementations to be separate. We will explain how we solved these challenges while minimizing the necessary changes to the CMSSW framework. We will also discuss on a concrete example how algorithms would offload work to NVIDIA GPUs using directly the CUDA API.

scholarly journals High-level programming for heterogeneous and hierarchical parallel systems

2018 ◽  
Vol 32 (6) ◽  
pp. 804-806
Author(s):  
Javier García-Blas ◽  
Christopher Brown
Keyword(s):  
Graphics Processing Units ◽  
Heterogeneous Computing ◽  
Timing Analysis ◽  
Parallel Systems ◽  
General Purpose ◽  
Ongoing Work ◽  
High Level ◽  
Tools And Techniques ◽  
Graphics Processing ◽  
Refactoring Tools

High-Level Heterogeneous and Hierarchical Parallel Systems (HLPGPU) aims to bring together researchers and practitioners to present new results and ongoing work on those aspects of high-level programming relevant, or specific to general-purpose computing on graphics processing units (GPGPUs) and new architectures. The 2016 HLPGPU symposium was an event co-located with the HiPEAC conference in Prague, Czech Republic. HLPGPU is targeted at high-level parallel techniques, including programming models, libraries and languages, algorithmic skeletons, refactoring tools and techniques for parallel patterns, tools and systems to aid parallel programming, heterogeneous computing, timing analysis and statistical performance models.

scholarly journals The Potential for a GPU-Like Overlay Architecture for FPGAs

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Jeffrey Kingyens ◽  
J. Gregory Steffan
Keyword(s):  
Graphics Processing Units ◽  
Programming Model ◽  
Instruction Level Parallelism ◽  
Floating Point ◽  
Register Files ◽  
High Level ◽  
Graphics Processing ◽  
Level Parallelism ◽  
Data Level ◽  
Accelerator System

We propose a soft processor programming model and architecture inspired by graphics processing units (GPUs) that are well-matched to the strengths of FPGAs, namely, highly parallel and pipelinable computation. In particular, our soft processor architecture exploits multithreading, vector operations, and predication to supply a floating-point pipeline of 64 stages via hardware support for up to 256 concurrent thread contexts. The key new contributions of our architecture are mechanisms for managing threads and register files that maximize data-level and instruction-level parallelism while overcoming the challenges of port limitations of FPGA block memories as well as memory and pipeline latency. Through simulation of a system that (i) is programmable via NVIDIA's high-levelCglanguage, (ii) supports AMD's CTM r5xx GPU ISA, and (iii) is realizable on an XtremeData XD1000 FPGA-based accelerator system, we demonstrate the potential for such a system to achieve 100% utilization of a deeply pipelined floating-point datapath.

scholarly journals Compass SPMD: a SPMD vectorized tracking algorithm

2020 ◽  
Vol 245 ◽  
pp. 01006
Author(s):  
Placido Fernandez Declara ◽  
J. Daniel Garcia
Keyword(s):  
Graphics Processing Units ◽  
Development Project ◽  
Tracking Algorithm ◽  
Level Trigger ◽  
Multiple Data ◽  
And Performance ◽  
Data Tracking ◽  
Gpu Architectures ◽  
High Level ◽  
Graphics Processing

Compass is a SPMD (Single Program Multiple Data) tracking algorithm for the upcoming LHCb upgrade in 2021. 40 Tb/s need to be processed in real-time to select events. Alternative frameworks, algorithms and architectures are being tested to cope with the deluge of data. Allen is a research and development project aiming to run the full HLT1 (High Level Trigger) on GPUs (Graphics Processing Units). Allen’s architecture focuses on data-oriented layout and algorithms to better exploit parallel architectures. GPUs already proved to exploit the framework efficiently with the algorithms developed for Allen, implemented and optimized for GPU architectures. We explore opportunities for the SIMD (Single Instruction Multiple Data) paradigm in CPUs through the Compass algorithm. We use the Intel SPMD Program Compiler (ISPC) to achieve good readability, maintainability and performance writing “GPU-like” source code, preserving the main design of the algorithm.

scholarly journals Artificial intelligence in clinical and genomic diagnostics

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Raquel Dias ◽  
Ali Torkamani
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Graphics Processing Units ◽  
Human Genetics ◽  
Individualized Medicine ◽  
Variant Calling ◽  
Clinical Diagnostics ◽  
Clinical Genomics ◽  
Diagnostic Modalities ◽  
Graphics Processing

AbstractArtificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.

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