Multimodality imaging. Volume 1, Deep learning applications. / Jasjit S. Suri and Mainak Biswas.

Format:

Book

Author/Creator:

Suri, Jasjit S., author.

Biswas, Mainak, author.

Series:

IOP Ebooks Series

Language:

English

Subjects (All):

Biomedical engineering.

Computer vision.

Deep learning (Machine learning).

Diagnostic imaging--Data processing.

Diagnostic imaging.

Physical Description:

1 online resource (354 pages)

Edition:

First edition.

Place of Publication:

Bristol, England : IOP Publishing, [2022]

Summary:

This book provides technical details of the application of deep learning and machine learning in medical imaging for diagnosis brain, cardiovascular, liver, lung diseases. The text also explores the technical and ethical aspects of deep learning and machine learning applications in medical imaging.

Contents:

Intro

Preface

Purpose

Content and organization

Editor biographies

Mainak Biswas

Jasjit S Suri

List of contributors

Chapter 1 Deep learning and augmented radiology

1.1 Introduction

1.2 The present

1.2.1 Fatty liver disease risk stratification

1.2.2 Carotid intima-media thickness (cIMT) measurement using DL for segmentation

1.2.3 Assessment of the treatment effects in acute ischemic stroke (AIS) using DL from MR images

1.2.4 Diagnosis of prostate cancer using DL

1.2.5 CT-based respiratory disease prognosis using DL

1.3 Future

1.3.1 DL in radiology

1.3.2 The future of radiology

1.4 The potential of deep learning

1.4.1 Economy

1.4.2 Augmented radiology and DL

1.4.3 Further development of DL

1.5 Challenges and risks in DL

1.5.1 Safety

1.5.2 Privacy

1.5.3 Legality

1.6 Conclusion

References

Chapter 2 Deep learning in biomedical imaging

2.1 Introduction

2.2 Deep learning models

2.2.1 Deep belief networks

2.2.2 Autoencoder

2.2.3 Convolutional neural networks

2.2.4 Deep residual network

2.3 DL based biomedical imaging systems

2.3.1 Cardiovascular application

2.3.2 Neurology application

2.3.3 Mammography application

2.3.4 Microscopy applications

2.3.5 Dermatology application

2.3.6 Gastroenterology applications

2.3.7 Pulmonary application

2.4 Discussion

2.4.1 Graphics processing unit and open-source software for deep learning

Chapter 3 A review of artificial intelligence in brain tumor classification and segmentation

3.1 Introduction

3.2 Brain cancer pathophysiology

3.2.1 Architecture at the cellular level

3.2.2 Links between brain tumors and genes

3.3 Imaging modality

3.3.1 Computed tomography imaging

3.3.2 Magnetic resonance imaging

3.3.3 Biopsy

3.3.4 Hyperstereoscopy imaging.

3.3.5 MR spectroscopy

3.4 Guidelines for tumor grading by the WHO

3.5 Brain tumor tests

3.5.1 Biomarker test

3.5.2 Biopsy

3.5.3 Imaging test

3.6 Classification methods

3.6.1 Machine learning

3.6.2 Deep learning

3.6.3 Brain image analysis using deep learning

3.6.4 A plausible solution for brain cancer classification

3.7 Brain cancer and other brain disorders

3.7.1 Stroke

3.7.2 Alzheimer's disease

3.7.3 Parkinson's disease

3.7.4 Leukoaraiosis

3.7.5 Multiple sclerosis

3.7.6 Wilson's disease

3.8 Discussion

3.8.1 A note on cancer detection biomarkers

3.9 Conclusion

Chapter 4 MRI based brain tumor classification and its validation: a transfer learning paradigm

4.1 Introduction

4.2 Background literature survey

4.3 Demographics and data preparation

4.3.1 Patient demographics

4.3.2 Data preparation

4.4 Methodology

4.4.1 CNN model

4.4.2 The architecture of AlexNet

4.4.3 Transfer learning and workflow

4.4.4 Weight optimization

4.4.5 A generalization system for tumor classification

4.5 Experimental protocol, results, and performance evaluation

4.5.1 Parameter selection and simulation

4.5.2 Results

4.5.3 Performance evaluation

4.6 Model validation and verification

4.6.1 Hypothesis validation

4.6.2 Software verification

4.7 Discussion

4.8 Conclusion

Appendix A

Chapter 5 Magnetic resonance based Wilson's disease tissue characterization in an artificial intelligence framework using transfer learning

5.1 Introduction

5.2 Background literature

5.3 Methodology

5.3.1 Patient demographics

5.3.2 Data augmentation

5.3.3 Pre-processing: skull and background removal

5.4 Global architecture: transfer learning

5.4.1 AlexNet

5.4.2 ResNet50

5.4.3 DenseNet161

5.4.4 XceptionNet.

5.4.5 InceptionV3

5.4.6 SivaSuriNet

5.5 Results

5.6 Characterization

5.6.1 Mean feature strength

5.6.2 Higher-order spectrum

5.7 Discussion

5.8 Conclusion

Chapter 6 Artificial intelligence based carotid plaque tissue characterisation and classification from ultrasound images using a deep learning paradigm

6.1 Introduction

6.2 Methodology

6.2.1 Patient demographics

6.2.2 Exclusion criteria

6.2.3 Ultrasound data acquisition and preprocessing

6.2.4 Plaque delineation

6.2.5 Ultrasound plaque data augmentation

6.2.6 Supercomputer specifications

6.2.7 Deep learning architecture

6.2.8 Experimental protocol

6.2.9 Machine learning for benchmarking deep learning

6.2.10 Performance parameters using the DL and ML methods

6.3 Results

6.3.1 Deep learning data analysis and benchmarking against machine learning

6.3.2 Plaque characterisation in a deep learning framework

6.4 Discussion

6.4.1 A note on the unbalanced datasets for symptomatic and asymptomatic plaques

6.4.2 Benchmarking against techniques available in the literature

6.4.3 A special note on the comparison of supercomputer hardware to a local machine

6.4.4 Strengths, weaknesses, and extensions

6.5 Conclusion

Disclosure/Conflict of interest

Appendix

Chapter 7 Quantification of plaque volume using a two-stage deep learning paradigm

7.1 Introduction

7.2 Background

7.3 Data acquisition

7.4 Methodology

7.5 Experimental protocol and results

7.6 Statistical tests

7.7 Discussion

7.8 Conclusion

Chapter 8 Stenosis measurement from ultrasound carotid artery images in the deep learning paradigm

8.1 Introduction

8.2 Patient demographics and image acquisition

8.3 Methodology

8.4 Experimental protocol, performance parameters, and results.

8.5 Statistical tests, variability and error bias analysis, and risk characterization

8.6 Discussion

8.7 Conclusion

Chapter 9 A systematic review of conventional and deep learning models for the measurement of plaque burden

9.1 Introduction

9.2 Chronological generation of cIMT regional segmentation and cIMT measurement

9.3 Ml application for cIMT and PA measurement

9.3.1 ANN model for cIMT region detection

9.3.2 Extreme learning machine radial basis neural network model for cIMT region detection

9.3.3 Fuzzy K-means classifier for cIMT region extraction

9.4 Deep learning application for cIMT and PA extraction

9.4.1 ANN autoencoder based cIMT region segmentation

9.4.2 Fully convolutional network for cIMT region estimation

9.4.3 FCN for PA measurement

9.4.4 Two-stage patching based AI model for cIMT and PA measurement

9.5 Discussion

9.5.1 Benchmarking

9.5.2 A short note on cardiovascular risk assessment

9.5.3 A note on the clinical impact of AI methods on cIMT/PA techniques

9.5.4 A note on inter- and intra-observer variability analysis on the evaluation of AI models

9.5.5 A short note on 10 year risk estimation using cIMT and PA

9.5.6 Statistical power analysis and diagnostic-odds ratio

9.6 Conclusions

Appendix A Mathematical representations of the ML and DL paradigms

Chapter 10 Ultrasound fatty liver disease risk stratification using an extreme learning machine framework

10.1 Introduction

10.2 Data demographics, collection, and preparation

10.2.1 Sub-sampling of US datasets (S4 and S8)

10.3 Methodology

10.3.1 The three-layered ELM architecture

10.3.2 Tissue characterization and risk stratification using ELM and SVM frameworks

10.3.3 Feature mining

10.4 Experimental protocol.

10.4.1 Experiment 1: the effect of the size of the training data on accuracy using four CV protocols

10.4.2 Experiment 2: the effect of the training set size using the sub-sampling strategy

10.4.3 Experiment 3: ELM and SVM time complexity

10.5 Results

10.5.1 Experiment 1: the effect of training data size on accuracy using the four CV protocols

10.5.2 Experiment 2: the effect of the training data size in parts during the CV protocols

10.5.3 Experiment 3: time comparison between ELM and SVM

10.6 Performance evaluations

10.6.1 ROC curves

10.6.2 Reliability and stability analysis

10.7 Discussion

10.7.1 Benchmarking

10.7.2 ELM and BPNN comparison

10.7.3 A special note on the ELM and SVM

10.7.4 Strengths, weaknesses, and future work

10.8 Conclusions

Appendix A Scientific validation

Appendix B Results of the ELM/SVM classifier for the S4 and S8 datasets

Chapter 11 Symtosis: deep learning based liver ultrasound tissue characterisation and risk stratification

11.1 Introduction

11.2 Patient demographics and acquisition

11.3 Methodology

11.3.1 Risk stratification model

11.3.2 CNN architecture

11.4 Results

11.4.1 Image pre-processing for the DL, ELM, and SVM

11.4.2 The effect of data size on stratification accuracy

11.4.3 Stratification analysis using the 'liver segregation index'

11.5 Performance evaluation: ROC, reliability, and timing analysis

11.5.1 ROC analysis

11.5.2 Reliability analysis

11.5.3 Timing analysis

11.6 Discussion

11.7 Conclusion

Chapter 12 Characterization of COVID-19 severity in infected lungs via artificial intelligence transfer learning

12.1 Introduction

12.2 Methodology

12.2.1 Patient demographics

12.2.2 Data acquisition

12.2.3 Baseline characteristics

12.2.4 Segmentation

12.2.5 Augmentation.

12.2.6 Models.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

9780750343459

0750343451

OCLC:

1429723431