Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

Wang, Shuihua; Tang, Chaosheng; Sun, Jianfang; Yang, Jingyuan; Huang, Chenxi; Phillips, P. Jonathon; Zhang, Yudong

doi:10.3389/fnins.2018.00818

Cited by 121 publications

(78 citation statements)

References 44 publications

(51 reference statements)

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“…DL solutions have been applied to medical image analysis [120] in very different topics related to classification or detection, such as identifying lung nodules into benign or malignant [121], alcoholism identification through brain MRI [122,123], multiple sclerosis detection [124], categorizing chest X-rays into different diseases [125], distinguishing patients with Alzheimer's disease versus normal [126], diagnosing diabetic retinopathy using digital photographs of the fundus of the eye [127], discriminating kidney cancer histopathological images into tumor or non-tumor [128], detecting cancerous lung nodules [129] and lung cancer stage [130] on CT scans, malignant skin cells on dermatological photographs [131], mitotic figures in breast histology images [132], or cell nuclei in colorectal adenocarcinoma histology image [133]. Regarding image segmentation, DL covers a variety of organs such as liver, prostate, spine, and knee cartilage both in CT and MRI [116,134].…”

Section: Biomedical Imagesmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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Section: Biomedical Imagesmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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“…Andere bereits bearbeitete Fragestellungen für Radiomics-Arbeiten waren die Differenzierung von MS und Erkrankungen aus dem Neuromyelitis-Optica-Spektrum [28][29][30] und die Abgrenzung von MS-Patienten von gesunden Kontrollprobanden. Zum letztgenannten Thema existieren auch auf Deep Learning beruhende Studien [31][32][33]. Eitel et al [34] untersuchten hierbei auch, welche Merkmale der Algorithmus zur Klassifikation heranzieht, und konnten so zeigen, dass neben den typischen Läsionen in geringerem Maß auch normal erscheinende Areale, wie z.…”

Section: Integration Klinischer Datenunclassified

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

Eichinger

Zimmer

Wiestler

2020

Rofo

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Background MR imaging is an essential component in managing patients with Multiple sclerosis (MS). This holds true for the initial diagnosis as well as for assessing the clinical course of MS. In recent years, a growing number of computer tools were developed to analyze imaging data in MS. This review gives an overview of the most important applications with special emphasis on artificial intelligence (AI). Methods Relevant studies were identified through a literature search in recognized databases, and through parsing the references in studies found this way. Literature published as of November 2019 was included with a special focus on recent studies from 2018 and 2019. Results There are a number of studies which focus on optimizing lesion visualization and lesion segmentation. Some of these studies accomplished these tasks with high accuracy, enabling a reproducible quantitative analysis of lesion loads. Some studies took a radiomics approach and aimed at predicting clinical endpoints such as the conversion from a clinically isolated syndrome to definite MS. Moreover, recent studies investigated synthetic imaging, i. e. imaging data that is not measured during an MR scan but generated by a computer algorithm to optimize the contrast between MS lesions and brain parenchyma. Conclusion Computer-based image analysis and AI are hot topics in imaging MS. Some applications are ready for use in clinical routine. A major challenge for the future is to improve prediction of expected disease courses and thereby helping to find optimal treatment decisions on an individual level. With technical improvements, more questions arise about the integration of new tools into the radiological workflow. Key Points: Citation Format

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“…Convolutional neural network (CNN), as one of the most widely used deep learning models, is always used. For example, Wang et al proposed a 14-layer CNN for multiple sclerosis identification [15]. For the seizure detection task, there are two ways of using the original EEG signals as the input image of CNN.…”

Section: Introductionmentioning

confidence: 99%

Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning

Zhang

Wang

Xiao

et al. 2020

Computational and Mathematical Methods in Medicine

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Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagnose epilepsy. Clinically reading tens or even hundreds of hours of EEG recordings is very time consuming. Therefore, automatic detection of seizure is of great importance. But the huge diversity of EEG signals belonging to different patients makes the task of seizure detection much challenging, for both human experts and automation methods. We propose three deep transfer convolutional neural networks (CNN) for automatic cross-subject seizure detection, based on VGG16, VGG19, and ResNet50, respectively. The original dataset is the CHB-MIT scalp EEG dataset. We use short time Fourier transform to generate time-frequency spectrum images as the input dataset, while positive samples are augmented due to the infrequent nature of seizure. The model parameters pretrained on ImageNet are transferred to our models. And the fine-tuned top layers, with an output layer of two neurons for binary classification (seizure or nonseizure), are trained from scratch. Then, the input dataset are randomly shuffled and divided into three partitions for training, validating, and testing the deep transfer CNNs, respectively. The average accuracies achieved by the deep transfer CNNs based on VGG16, VGG19, and ResNet50 are 97.75%, 98.26%, and 96.17% correspondingly. On those results of experiments, our method could prove to be an effective method for cross-subject seizure detection.

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Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

Cited by 121 publications

References 44 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning

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