What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. 
Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

An overview of deep learning in medical imaging focusing on MRI

In studying the performance of coded communications over memoryless channels (with or without fading), the results are given as upper bounds on the average bit error probability (BEP). In principle, there are three different approaches to arriving at these bounds, all of which employ obtaining the so-called pairwise error probability , or the probability of choosing one symbol sequence over another for a given pair of possible transmitted symbol sequences, followed by a weighted summation over all pairwise events. In this chapter, we will focus on the results obtained from the third approach since these provide the tightest upper bounds on the true performance. The first emphasis will be placed on evaluating the pairwise error probability with and without CSI, following which we shall discuss how the results of these evaluations can be used via the transfer bound approach to evaluate average BEP of coded modulation transmitted over the fading channel.

Coded Communication over Fading Channels

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Deep learning has developed as an effective machine learning method that takes in numerous layers of features or representation of the data and provides state-of-the-art results. The application of deep learning has shown impressive performance in various application areas, particularly in image classification, segmentation and object detection. Recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we provide a detailed review of various deep architectures and model highlighting characteristics of particular model. Firstly, we described the functioning of CNN architectures and its components followed by detailed description of various CNN models starting with classical LeNet model to AlexNet, ZFNet, GoogleNet, VGGNet, ResNet, ResNeXt, SENet, DenseNet, Xception, PNAS/ENAS. We mainly focus on the application of deep learning architectures to three major applications, namely (i) wild animal detection, (ii) small arm detection and (iii) human being detection. A detailed review summary including the systems, database, application and accuracy claimed is also provided for each model to serve as guidelines for future work in the above application areas.

Convolutional neural network: a review of models, methodologies and applications to object detection

Super resolution problems are widely discussed in medical imaging Spatial resolution of medical images are not sufficient due to the constraints such as image acquisition time, low irradiation dose or hardware limits To address these problems, different super resolution methods have been proposed, such as optimization or learning-based approaches Recently, deep learning methods become a thriving technology and are developing at an exponential speed We think it is necessary to write a review to present the current situation of deep learning in medical imaging super resolution In this paper, we first briefly introduce deep learning methods, then present a number of important deep learning approaches to solve super resolution problems, different architectures as well as up-sampling operations will be introduced Afterwards, we focus on the applications of deep learning methods in medical imaging super resolution problems, the challenges to overcome will be presented as well

A Review of the Deep Learning Methods for Medical Images Super Resolution Problems

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

https://iopscience.iop.org/article/10.1088/1361-6560/aab9e9/pdf

Super-resolution reconstruction of MR image with a novel residual learning network algorithm.

The material attributes of foreign object debris (FOD) are the most crucial factors to understand the level of damage sustained by an aircraft. However, the prevalent FOD detection systems lack an effective method for automatic material recognition. This paper proposes a novel FOD material recognition approach based on both transfer learning and a mainstream deep convolutional neural network (D-CNN) model. To this end, we create an FOD image dataset consisting of images from the runways of Shanghai Hongqiao International Airport and the campus of our research institute. We optimize the architecture of the D-CNN by considering the characteristics of the material distribution of the FOD. The results show that the proposed approach can improve the accuracy of material recognition by 39.6% over the state-of-the-art method. The work here will help enhance the intelligence capability of future FOD detection systems and encourage other practical applications of material recognition technology.

/pdf/foreign-object-debris-material-recognition-based-on-4m1hcej0wf.pdf

Foreign object debris material recognition based on convolutional neural networks

The next-generation wireless sensor network (WSN) has the capability of carrying hundreds of high-definition video streams, beside the feature of massive employment of energy-efficient nodes. However, several challenges are identified with respect to the video bearing, such as the different video formats, enormous size of “raw” video, and compatibility with heterogeneous terminal devices. The video transcoding system (VTS) is widely believed to address these challenges. This paper introduces a cloud-based, more specifically, Hadoop-based, video transcoding system to fulfill the vision of bearing hundreds of HD video streams in the next generation WSN, with a discussion on optimization of several significant parameters. This paper obtains three remarkable results: (1) there is an optimal value of the number of Mappers; (2) the optimal value is closely related to the file size; (3) the transcoding time depends principally on the duration of video files rather than their sizes.

Design and Experiment Analysis of a Hadoop-Based Video Transcoding System for Next-Generation Wireless Sensor Networks

A hierarchical softcell wireless network and a access control method therefore are provided. The network comprises a signal covering plane, a softcell plane and a service requirement plane, wherein a control center controls wireless resource use and operate mode of distributed antennas to form multi-layer softcells overlapping geographically; service requirement plane centralizes services of different velocity scenes distributed geographically; the access control method of the hierarchical softcell network according to the present invention mapps a user terminal to a certain softcell in the softcells that is suitable for the terminal to service based on mapping relationship between velocity feature and softcell layer by estimating the velocity of the terminal. The hierarchical softcell wireless network considers network performance, practicability and complexity synthetically, and can reduce system interference, use wireless resource efficiently, equalize loads, control handover load level of the network and improve system capacity and service quality.

Hierarchical softcell wireless network and access control method therefore

In this paper, we propose a novel framework of Foreign Object Debris (FOD) classification system. The system contains a FOD detection subsystem, electro-optical subsystem and the control center. The system not only provides continuous surveillance of scanned surfaces and achieves the goal of FOD detection, but also performs FOD classification. Both low level features and subspace features are compared to extract the FOD. Multiclass classifiers are trained in all the candidate feature spaces with the Support Vector Machine (SVM) to classify FOD. Experimental results show that it is promising to classify FOD with low-level features.

Haoyu Xu

Papers

Super-resolution reconstruction of MR image with a novel residual learning network algorithm.

Foreign object debris material recognition based on convolutional neural networks

Design and Experiment Analysis of a Hadoop-Based Video Transcoding System for Next-Generation Wireless Sensor Networks

Hierarchical softcell wireless network and access control method therefore

A Novel FOD Classification System Based on Visual Features