Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods: With Deep Learning Methods

Description

Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases. Front Cover Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods Copyright Page Contents List of contributors 1 Introduction to deep learning and diagnosis in medicine Introduction Deep learning architectures Convolutional neural network AlexNet ZFNet NiN VGGNet Inception (GoogLeNet) ResNet DenseNet U-Net SegNet R-CNN YOLO Other convolutional neural networks algorithms Recurrent neural network Long short-term memory Gated recurrent unit Bidirectional recurrent neural network Boltzmann machine and restricted Boltzmann machines Autoencoder Generative adversarial network Semisupervised GAN, bidirectional GAN Conditional GAN, InfoGAN, AC-GAN LAPGAN, DCGAN, BEGAN SAGAN, BigGAN WGAN, WGAN-GP, LSGAN PROGAN, StyleGAN, StyleGAN2 Comparisons of some GAN models Other architectures Deep belief network Capsule network Hybrid architectures Application fields of deep learning in medicine Clinical and medical images Biosignals Biomedicine Electronic health records Other fields Conclusions References 2 One-dimensional convolutional neural network-based identification of sleep disorders using electroencephalogram signals Introduction Materials and methods Dataset Method Results Discussions Conclusions References 3 Classification of histopathological colon cancer images using particle swarm optimization-based feature selection algorithm Introduction Methodology Dataset preparation Data preprocess and feature extraction Data size reduction Global feature extraction Classifier Gradient boosting Feature selection Particle swarm optimization Performance metrics Results Classification results Models complexity comparison SHAP analysis Receiver operator characteristic analysis Comparison Discussion Conclusion References 4 Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks Introduction Definition of problem Materials and methods Dataset Oversampling 1D-CNN architecture 1D convolution layer Pooling layer Batch normalization and dropout layers Experimental result Performance metrics Experimental environment Random forest classifier 1D-CNN VGG16 classifier results Discussion Conclusion and future direction References 5 Patch-based approaches to whole slide histologic grading of breast cancer using convolutional neural networks Introduction and motivation Tubular formation Nuclear pleomorphism Mitotic figure detection and classification Challenges in obtaining Nottingham grading score Challenges in nuclear pleomorphism classification Challenges in detection/segmentation of tubular formation Challenges in mitotic classification Literature review and state of the art AI-based approaches for nuclear pleomorphism classification AI-based approaches for detection and segmentation of tubular formation AI-based approaches for mitotic classification and counting Problem/system/application definition Problem definition and description System and application definition Proposed methodology Pre-processing Deep learning methods Mitosis detection and classification Tubule segmentation Pleomorphism classification Results and discussions Dataset Assessment Quantitative assessment Qualitative assessment Conclusions Future work References 6 Deep neural architecture for breast cancer detection from medical CT image modalities Introduction Related work Experimental work Dataset Work flow Image pre-processing and augmentation methods Models explored Experimental results Evaluation parameters Models performance on BreakHis dataset Models performance on BACH2018 dataset Conclusion References 7 Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility... Introduction and motivation Literature review and state of the art Pre-processing of PCM time-lapse images Segmentation of PCM time-lapse images Tracking and quantification from PCM time-lapse images Workflows for the analysis of PCM time-lapse images Problem definition, acquisition and annotation of data Data acquisition Data annotation Proposed solution Pre-processing Segmentation Tracking and quantification Qualitative and quantitative analysis Pre-processing Segmentation Tracking and quantification Use cases and applications Discussion Conclusions Outlook and future work Software availability Acknowledgment References 8 Automatic detection of pathological changes in chest X-ray screening images using deep learning methods Introduction Screening for lung abnormalities Introduction Original image data Normal cases Pathological cases Image data preprocessing Methods Results Local conclusions Detecting extrapulmonary pathologies Introduction Data preparation Computational experiment Local conclusions Identification of subjects with lung roots abnormalities Introduction Materials Methods Results Local conclusions Chest X-ray image analysis web services Overview Authentication Authentication Input data validation X-ray modality checker for 2D images Anatomy checker for 2D images Axes order and orientation checker for 2D chest X-ray images Resources management Applications for processing and analyzing chest X-ray Lung segmentation on chest X-rays Detecting abnormalities in chest X-rays (heatmap) Application for computer-aided diagnostics based on chest X-ray Conclusion References 9 Dependence of the results of adversarial attacks on medical image modality, attack type, and defense methods Introduction Materials Chest X-ray images CT images Histopathology images Methods Attacks FGSM Attacks AutoAttacks Carlini-Wagner Attacks Defenses Adversarial training High-level representation guided denoiser The MagNet Experimental pipeline Results Experiments with X-ray images Experiments with computer tomography images Experiments with histopathology images Discussion The abilities of adversarial training defense method Important properties of class-label-guided denoiser defense Important properties of MagNet defense Conclusions References 10 A deep ensemble network for lung segmentation with stochastic weighted averaging Introduction Related works Proposed system Dataset collection Data augmentation Segmentation architectures HarDNet UNet++ Deeplab V3—ResNet Stochastic weighted averaging (SWA) Ensemble Results and discussion Dataset description Ablation studies Analysis of HarDNet Analysis of UNet++ Analysis of ResNet Analysis of ensemble Performance analysis Conclusion References 11 Deep ensembles and data augmentation for semantic segmentation Introduction Methods Deep learning for semantic image segmentation Loss functions Dice Loss Tversky Loss Focal Tversky Loss Focal Generalized Dice Loss Log-Cosh Type Losses SSIM Loss Different functions combined loss Data augmentation Shadows Contrast and motion blur Color mapping Experimental results Metrics Datasets and testing protocol for polyp segmentation Datasets and testing protocol for skin segmentation Datasets and testing protocol for leukocyte segmentation Experiments Conclusions Acknowledgment References 12 Classification of diseases from CT images using LSTM-based CNN Introduction Background CT dataset-issues and challenges in handling them Elucidating classical CNN- and LSTM-based CNN models Convolutional neural network Convolution layer Pooling layer Fully connected layers LSTM networks Previous work done on CNN-LSTM Conclusion References 13 A novel polyp segmentation approach using U-net with saliency-like feature fusion Introduction Methodology Image enhancement Discriminatory feature matrices Fusion of feature matrices U-net fine-tuning Loss function Experiments and results Datasets Evaluation metrics Experimental results of enhanced images with image inpainting method Experimental results of proposed method Discussion Conclusion Compliance with ethical standards Conflict of interest Human and animal rights References Index Back Cover