Deep Learning in Medical Image Analysis

Editorial begins: Over recent years, deep learning (DL) has established itself as a powerful tool across a broad spectrum of domains in imaging—e.g., classification, prediction, detection, segmentation, diagnosis, interpretation, reconstruction, etc. While deep neural networks were initially nurtured in the computer vision community, they have quickly spread over medical imaging applications

Medical Big Data and Artificial Intelligence for Healthcare

Big data have altered the way we manage, explore, evaluate, analyze, and leverage data across many different industries [...

Measuring system entropy with a deep recurrent neural network model

In this paper, a methodology for assessing the unpredictability of systems with memory was developed. The proposed approach consists in approximating the probability distribution exhibited by the response of a system, understood as a stochastic process, with a deep recurrent neural network; such networks offer increased forecasting capability by exploiting an accumulative register of previous system states. Once the probability distribution is computed, the uncertainty or entropy of the underlying process is measured. This measure determines the degree of regularity in the system, and identifies how atypical the system dynamics are. The proposed model was validated by identifying industrial gas turbine engine faults from recorded sensor data

A Review of Deep-Learning-Based Medical Image Segmentation Methods

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems

A Review on Serious Games for Dementia Care in Ageing Societies

Dementia is a severe disease threatening ageing societies, which not only causes great harm to patients both physically and psychologically but also places a heavy burden on patients' families. Medications have been used for the treatment of dementia but with little success. However, serious games, as a new form of dementia therapy, stand out from various therapeutic methods and pave the way for dementia treatment. In the field of serious games for dementia care (SGDC) in ageing societies, there exists abundant research related to this topic. While, a detailed review of the development route and a category framework for characteristics of dementia are still needed. Besides, due to the large number of games, it is difficult to select out effective ones. Yet, there is no unified and comprehensive assessment methods for SGDC. So a reliable assessment model is worth studying. In this paper, we review these existing research work on SGDC from two perspectives: (1) the development of SGDC; (2) the different symptoms in different dementia stages. We also propose a comprehensive and professional assessment model of the therapeutic effectiveness of SGDC to compensate for the simplicity of existing assessment methods. Finally, a discussion related to SGDC is presented

MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection

Sensorineural hearing loss (SNHL) is a common hearing disorder or deafness which accounts for about 90% of the reported hearing loss. Magnetic resonance imaging (MRI) has been found to be an effective neuroimaging technique for detecting SNHL. However, manual detection methods, mainly based on the visual inspection of MRI, are cumbersome, time-consuming and need skilled supervision. Hence, there is a great need to design a computer-aided detection system for fast, accurate and automated detection of SNHL. This paper presents a new method for automated diagnosis of SNHL through brain MR images. Fast discrete curvelet transform is employed for image decomposition. The features are extracted from various decomposed subbands at different scales and orientations. A set of discriminant features is then derived using PCA+LDA algorithm. A hybrid classifier is suggested by integrating extreme learning machine and Jaya optimization with mutation (MJaya-ELM) to distinguish hearing loss images from healthy MR images. The proposed hybrid method overcomes the drawbacks of traditional ELM and other learning algorithms for single layer feedforward neural network. The concept of mutation is introduced to conventional Jaya optimization (MJaya) for improving the global search ability of the solutions by providing additional diversity. The proposed system is evaluated on a well-studied database. The comparison results demonstrate that the proposed scheme outperforms the existing schemes in terms of overall accuracy and sensitivity over different classes. The effectiveness of the proposed MJaya-ELM algorithm is also compared with its counterparts such as PSO-ELM, DE-ELM, and Jaya-ELM, and the results indicate the superiority of MJaya-ELM

In Search of the Max Coverage Region in Road Networks

The widespread use of mobile devices has resulted in the generation of vast amounts of spatial data. The availability of such large-scale spatial data facilitates the development of data-driven approaches to address real-life problems. This paper introduces the max coverage region (MCR) problem in road networks and provides efficient solutions. Given a set of spatial objects and a coverage radius, the MCR problem aims to identify a location from the road network, so that we can reach as many spatial objects as possible within the given coverage radius from the location. This problem is fundamental to supporting many real-world applications. Given a road network and a set of sensors, this problem can be used to find the best location for a sensor maintenance station. This problem can also be applied in medical research, such as in a protein–protein interaction network, where the nodes represent proteins, the edges represent their interactions, and the weight of an edge represents confidence. We can use the MCR problem to find the set of interacting proteins with a confidence budget. We propose an efficient exact solution to solve the problem, where we reduce the MCR problem to an equivalent problem named the most overlapped interval and design an edge-level upper bound estimation method to reduce the search space. Furthermore, we propose two approximate solutions that sacrifice a little accuracy for much better efficiency. Our experimental study on real-road network datasets demonstrates the effectiveness and superiority of the proposed approaches

GFNet: A Deep Learning Framework for Breast Mass Detection

Background: Breast mass is one of the main symptoms of breast cancer. Effective and accurate detection of breast masses at an early stage would be of great value for clinical breast cancer analysis. Methods: We developed a novel mass detection framework named GFNet. The GFNet is comprised of three modules, including patch extraction, feature extraction, and mass detection. The developed breast mass detection framework is of high robustness and generality that can be self-adapted to images collected by different imaging devices. The patch-based detection is deployed to improve performance. A novel feature extraction technique based on gradient field convergence features (GFCF) is proposed to enhance the information of breast mass and, therefore, provide useful information for the following patch extraction module. A novel false positives reduction method is designed by combining the texture and morphological features in breast mass patch. This is the first attempt at fusing morphological and texture features for breast mass false positive reduction. Results: Compared to other state-of-the-art methods, the proposed GFNet showed the best performance on CBIS-DDSM and INbreast with an accuracy of 0.90 at 2.91 false positive per image (FPI) and 0.99 at only 0.97 FPI, respectively. Conclusions: The GFNet is an effective tool for detecting breast mass

Schematic diagram of the modified two-compartment model with impulse residue function for glomerular filtration.

<p>A is the vascular compartment includes intrarenal arteries and glomerular vessels, and T is the tubules compartment. The retention function R_A and R_T in compartment A and T (represented in solid arrow within the box) are the convolution of the input and each impulse residue function. Dashed lines denote the outflow of each compartment and the outflow of compartment A partially flows into compartment T.</p

A Privacy-Preserving Intelligent Medical Diagnosis System Based on Oblivious Keyword Search

One of the concerns people have is how to get the diagnosis online without privacy being jeopardized. In this paper, we propose a privacy-preserving intelligent medical diagnosis system (IMDS), which can efficiently solve the problem. In IMDS, users submit their health examination parameters to the server in a protected form; this submitting process is based on Paillier cryptosystem and will not reveal any information about their data. And then the server retrieves the most likely disease (or multiple diseases) from the database and returns it to the users. In the above search process, we use the oblivious keyword search (OKS) as a basic framework, which makes the server maintain the computational ability but cannot learn any personal information over the data of users. Besides, this paper also provides a preprocessing method for data stored in the server, to make our protocol more efficient