State of the art in neural networks and their applications. Volume 2

State of the art in neural networks and their applications Volume 2 Volume 2 /

State of the Art in Neural Networks and Their Applications, Volume Two presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. The book provides over views and case studies of advances in the role of machine learning, artificial in...

Descripción completa

Detalles Bibliográficos
Otros Autores: S. El-Baz, Ayman, editor (editor), Suri, Jasjit S., editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England ; San Diego, California : Academic Press [2023]
Materias:
Diagnostic imaging > Data processing.
Image analysis.
Neural networks (Computer science) > Industrial applications.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835420606719
Tabla de Contenidos:
  • Front Cover
  • State of the Art in Neural Networks and Their Applications
  • Copyright Page
  • Dedication
  • Contents
  • List of contributors
  • About the editors
  • Acknowledgments
  • 1 Microscopy Cancer Cell Imaging in B-lineage Acute Lymphoblastic Leukemia
  • 1.1 Introduction
  • 1.2 Building a computer-assisted solution
  • 1.3 Data preparation
  • 1.3.1 Preparation of slide for microscopic imaging
  • 1.3.2 Capture of microscopic images from healthy and cancer subjects for B-acute lymphoblastic leukemia cancer
  • 1.4 Normalization of color stain to correct for abnormalities during the staining process
  • 1.4.1 Quantitative results
  • 1.5 Segmentation of cells of interest (in B-lineage ALL cancer)
  • 1.5.1 Method-1 of cell segmentation using traditional image processing techniques
  • 1.5.2 Method-2 of cell segmentation using deep belief network
  • 1.5.3 Method-3 of cell segmentation using novel convolutional neural network architecture
  • 1.5.3.1 Brief review of convolutional neural network architectures
  • 1.5.3.2 Semantic versus instance segmentation in medical imaging
  • 1.5.3.3 Method-3: novel proposed EDNiS-Net convolutional neural network for automated nuclei instance segmentation
  • 1.5.3.3.1 Base module
  • 1.5.3.3.2 Encoder module
  • 1.5.3.3.3 Decoder module
  • 1.5.3.3.4 Proposed loss function
  • 1.5.3.3.5 Results and discussion
  • 1.5.3.4 Region-proposal based convolutional neural network architectures
  • 1.6 Classification of cancer and healthy cells
  • 1.6.1 C-NMC 2019 challenge dataset
  • 1.6.2 Classification on C-NMC 2019 dataset
  • 1.6.3 SDCT-AuxNetθ CNN architecture for C-NMC 2019 dataset
  • 1.7 Conclusions
  • References
  • 2 Computational imaging applications in brain and breast cancer
  • 2.1 Introduction
  • 2.2 Building upon current clinical standards
  • 2.2.1 Clinical standards
  • 2.2.2 Tissue segmentation.
  • 2.3 Deep learning applications in brain cancer
  • 2.3.1 Tumor grading
  • 2.3.2 Survival analysis
  • 2.3.3 Radiogenomics
  • 2.3.3.1 1p/19q
  • 2.3.3.2 Isocitrate dehydrogenase
  • 2.3.3.3 6-methylguanine-DNA methyltransferase
  • 2.3.4 Pseudoprogression
  • 2.4 Deep learning applications in breast cancer
  • 2.4.1 Increasing accuracy in breast cancer risk assessment
  • 2.4.2 Reproducible breast density assessment for improved breast cancer risk prediction
  • 2.4.3 Improving performance in breast cancer diagnosis
  • 2.4.4 Enhancing efficacy in breast cancer clinical practice
  • 2.5 Conclusion
  • Acknowledgments
  • References
  • 3 Deep neural networks and advanced computer vision algorithms in the early diagnosis of skin diseases
  • 3.1 Introduction and motivation for the early diagnosis of melanoma
  • 3.2 Artificial intelligence and computer vision in melanoma diagnosis
  • 3.3 Medical diagnostic procedures for screening of skin diseases
  • 3.4 State-of-the-art survey on skin mole segmentation methods
  • 3.4.1 Comparison of the state of the art
  • 3.4.2 Summary
  • 3.5 Improved local and global patterns detection algorithms by deep learning algorithms
  • 3.6 Early classification of skin melanomas in dermoscopy
  • 3.6.1 Diagnostic algorithms
  • 3.6.2 Approaches to detect the diagnostic criteria
  • 3.6.3 Approaches to directly classify skin conditions
  • 3.6.3.1 Classifiers utilizing the convolutional neural networks as a feature extractor
  • 3.6.3.2 Classifiers using end-to-end learning convolutional neural networks model training with transfer learning
  • 3.6.3.3 Convolutional neural networks model training from scratch
  • 3.6.3.4 Ensembles of convolutional neural networks models
  • 3.7 Conclusions
  • 3.8 How to speed up the classification process with field-programmable gate arrays?
  • 3.9 Challenges and future directions
  • 3.10 Teledermatology.
  • References
  • 4 An accurate deep learning-based computer-aided diagnosis system for early diagnosis of prostate cancer
  • 4.1 Introduction
  • 4.2 Methods
  • 4.2.1 Feature Extraction
  • 4.2.2 CNN-based classification
  • 4.3 Experimental results
  • 4.4 Conclusion
  • References
  • 5 Adaptive graph convolutional neural network and its biomedical applications
  • 5.1 Introduction
  • 5.2 Related work
  • 5.2.1 Evolution of graph convolutional neural networks
  • 5.2.1.1 Spatial graph convolutional neural networks
  • 5.2.1.2 Spectral graph convolutional neural networks
  • 5.2.2 Neural network on molecular graph
  • 5.2.3 Attention on graph
  • 5.2.4 Neural network for survival analysis
  • 5.3 Method
  • 5.3.1 Spectral graph convolution-LL layer
  • 5.3.1.1 Learning residual graph Laplacian
  • 5.3.1.2 Re-parameterization on feature transform
  • 5.3.2 Adaptive graph convolution network architecture
  • 5.3.3 Graph attention network on adaptive graph
  • 5.3.4 DeepGraphSurv framework
  • 5.4 Experiment
  • 5.4.1 Drug-property prediction
  • 5.4.1.1 Baseline model
  • 5.4.1.2 Dataset
  • 5.4.1.3 Experimental result
  • 5.4.2 DeepGraphSurv and survival prediction
  • 5.4.2.1 Dataset
  • 5.4.2.2 Baseline model
  • 5.4.2.3 Experimental result
  • 5.5 Conclusion
  • References
  • Further reading
  • 6 Deep slice interpolation via marginal super-resolution, fusion, and refinement
  • 6.1 Introduction
  • 6.2 Related work
  • 6.2.1 Traditional slice interpolation methods
  • 6.2.2 Learning-based super-resolution methods
  • 6.3 Problem formulation and baseline convolutional neural networks approaches
  • 6.4 The proposed algorithm
  • 6.4.1 Marginal super-resolution
  • 6.4.2 Two-view fusion and refinement
  • 6.4.3 Comparison with baseline convolutional neural networks approaches
  • 6.5 Experiments
  • 6.5.1 Implementation details
  • 6.5.2 Dataset
  • 6.5.3 Evaluation metrics.
  • 6.5.4 Visual comparisons
  • 6.5.5 Ablation study
  • 6.6 Conclusion
  • References
  • 7 Explainable deep learning approach to predict chemotherapy effect on breast tumor's MRI
  • 7.1 Introduction
  • 7.2 Materials and developed methods
  • 7.2.1 Study population
  • 7.2.2 Magnetic resonance imaging protocol
  • 7.2.3 Image preprocessing
  • 7.2.4 Convolution neural network architecture development
  • 7.3 Results
  • 7.3.1 Quantitative results
  • 7.3.2 Qualitative results
  • 7.4 Discussion
  • 7.5 Conclusion
  • Aknowledgments
  • References
  • 8 Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features
  • 8.1 Introduction
  • 8.2 Related work on interpretable artificial intelligence
  • 8.2.1 Motivations
  • 8.2.2 Related terminology
  • 8.2.3 Related work on explainable artificial intelligence
  • 8.2.3.1 Explainable artificial intelligence for medical applications
  • 8.2.3.2 Visualization methods and feature attribution
  • 8.2.3.3 Concept attribution
  • 8.2.4 Evaluation of explainable artificial intelligence methods
  • 8.3 Methods
  • 8.3.1 Retinopathy of prematurity
  • 8.3.1.1 Relevant background
  • 8.3.1.2 Dataset for the experiments
  • 8.3.1.3 Task and classification model
  • 8.3.2 Concept attribution with regression concept vectors
  • 8.3.2.1 Identification of the concepts
  • 8.3.2.2 Computing the regression concept vector
  • 8.3.2.3 Generating local explanations by conceptual sensitivity
  • 8.3.2.4 Agglomerating scores for global explanations
  • 8.4 Experiments and results
  • 8.4.1 Network performance on the retinopathy of prematurity task
  • 8.4.2 Results of concept attribution
  • 8.4.2.1 Identification of the concepts
  • 8.4.2.2 Computation of the regression concept vectors
  • 8.4.2.3 Evaluation of the conceptual sensitivities
  • 8.4.2.4 Global explanations with Br
  • 8.5 Discussion of the results.
  • 8.6 Conclusions
  • Acknowledgments
  • References
  • 9 Computational lung sound classification: a review
  • 9.1 Introduction
  • 9.2 Data processing
  • 9.2.1 Audio signal preprocessing
  • 9.2.1.1 Signal splitting
  • 9.2.1.2 Noise filtering
  • 9.2.1.3 Resampling
  • 9.2.1.4 Amplitude scaling
  • 9.2.1.5 Segment splitting
  • 9.2.1.6 Padding
  • 9.2.2 Feature extraction
  • 9.2.2.1 Features for conventional classifiers
  • 9.2.2.2 Time-frequency representations for deep learning
  • 9.2.3 Data augmentation
  • 9.2.3.1 Time domain
  • 9.2.3.2 Time-frequency domain
  • 9.3 Data modeling
  • 9.3.1 Machine learning
  • 9.3.1.1 Conventional classifiers
  • 9.3.1.2 Deep learning architectures
  • 9.3.1.2.1 Convolutional neural networks
  • 9.3.1.2.2 Recurrent networks
  • 9.3.1.2.3 Hybrid systems
  • 9.3.2 Learning paradigm
  • 9.3.2.1 Transfer learning
  • 9.3.2.2 Postprocessing
  • 9.4 Recent public lung sound datasets
  • 9.4.1 ICBHI 2017 dataset
  • 9.4.2 The Abdullah University Hospital 2020 dataset
  • 9.4.3 HF_Lung_V1 dataset
  • 9.5 Conclusion
  • References
  • 10 Clinical applications of machine learning in heart failure
  • 10.1 Introduction
  • 10.2 Diagnosis
  • 10.2.1 Automatic diagnosis, classification, and phenotyping of heart failure
  • 10.2.2 Detection of heart failure-associated arrhythmia
  • 10.3 Management
  • 10.3.1 Prognostic prediction
  • 10.3.2 Development of therapy
  • 10.3.3 Optimal patient selection for specific therapies or recommendation of optimal therapy
  • 10.4 Prevention
  • 10.5 Conclusion
  • References
  • 11 Role of artificial intelligence and radiomics in diagnosing renal tumors: a survey
  • 11.1 Introduction
  • 11.2 Basic background
  • 11.2.1 Deep learning
  • 11.2.2 Machine learning
  • 11.2.3 Radiomics
  • 11.3 Steps of artificial intelligence-based diagnostic systems
  • 11.3.1 Image acquisition
  • 11.3.2 Image segmentation.
  • 11.3.3 Feature extraction and qualifications.

Ejemplares similares