Artificial intelligence in cancer diagnosis and prognosis. Volume 3, Brain and prostate cancer. / edited by Ayman El-Baz and Jasjit S Suri.

Format:

Book

Contributor:

El-Baz, Ayman, editor.

Suri, Jasjit S., editor.

Series:

IPEM-IOP Series in Physics and Engineering in Medicine and Biology Series

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Biomedical engineering.

Physical Description:

1 online resource (304 pages)

Edition:

First edition.

Place of Publication:

Bristol, England : IOP Publishing, [2022]

Summary:

This book focuses on major trends and challenges in the area of diagnosing lung and kidney cancer using artificial intelligence tools. It presents work aimed to identify new techniques and their use in biomedical analysis.

Contents:

Intro

Preface

Acknowledgements

Editor biographies

Ayman El-Baz

Jasjit S Suri

List of contributors

Outline placeholder

Adeel Ahmed Abbasi

Nahla B Abdel-Hamid

H Arafat Ali

Sarah M Ayyad

Samir Kumar Bandyopadhyay

Gustavo M Callico

Daniel U Campos-Delgado

Inés Alejandro Cruz-Guerrero

Dimitrios E Diamantis

Shawni Dutta

Mohamed Abou El-Ghar

Moumen El-Melegy

Himar Fabelo

Davide Fontanarosa

Matthew Foote

Mohamed Ghazal

Preetam Ghosh

Vishal Goyalis

Cheng-Yeh Hsieh

Lal Hussain

Jiwoong Jason Jeong

Rishabh Kapoor

Ali Keles

Ayturk Keles

Labib M Labib

Rui Li

Tian Liu

Zecheng Liu

Chung-Ming Lo

Yeh-Chi Lo

Ali Mahmoud

Hui Mao

Aldo Rodrigo Mejia-Rodríguez

Akash Mehta

Serafeim Moustakidis

Charis Ntakolia

Samuel Ortega

Jatinder R Palta

Elpiniki I Papageorgiou

Nikolaos Papandrianos

Ben Perrett

Mark Pinkham

Prabhakar Ramachandran

Venkatakrishnan Seshadri

Ahmed Shalaby

Mohamed Shehata

Ren-Dih Sheu

William C Sleeman IV

Sriram Srinivasan

Richard Stock

James Tam

Zhen Tian

Tzu-Chi Tseng

Jia Wei

Lei Yang

Xiaofeng Yang

Wenguang Yuan

Yading Yuan

Chapter 1 Artificial Intelligence in prostate cancer treatment with image-guided radiation therapy

1.1 Introduction

1.1.1 External radiation therapy for prostate cancer

1.1.2 Brachytherapy for prostate cancer: radioactive seed implants

1.2 Deep contouring: automated multiple organ segmentation using dilated U-Net with generalized Jaccard distance

1.2.1 Introduction

1.2.2 Methodology

1.2.3 Experiments

1.2.4 Summary

1.3 Deep planning: fully 3D-knowledge-based treatment planning

1.3.1 Introduction

1.3.2 Methodology

1.3.3 Experiments

1.3.4 Summary

1.4 Conclusions

References.

Chapter 2 Artificial-intelligence-based diagnosis of brain tumor diseases

2.1 Introduction

2.2 Related works

2.3 Current methods used to collect images

2.3.1 Ultrasound (USG)

2.3.2 Projection radiography (x-rays)

2.3.3 Computed tomography

2.3.4 Magnetic resonance imaging

2.3.5 Positron emission tomography

2.4 Background

2.4.1 Artificial intelligence and machine learning

2.4.2 Performance evaluation metrics

2.5 Datasets of brain tumors

2.6 Proposed methodologies for disease detection

2.6.1 Brain tumor detection methodology

2.7 Experimental results

2.8 Conclusions

References

Chapter 3 Multisite brain tumor segmentation using a unified generative adversarial network

3.1 Introduction

3.2 UGAN

3.2.1 Method overview

3.2.2 Loss function

3.3 Experiments

3.3.1 Datasets

3.3.2 Training settings

3.3.3 Segmentation performances

3.4 Conclusions

References and further reading

Chapter 4 Role of artificial intelligence in automatic segmentation of brain metastases for radiotherapy

4.1 Introduction

4.1.1 Brain metastasis treatment options

4.2 Manual segmentation of tumors

4.2.1 Limitations of manual segmentation

4.3 Automatic segmentation

4.3.1 Automatic segmentation techniques

4.3.2 U-Net

4.3.3 Identification of small lesions

4.3.4 Post-treatment volumetric assessment

4.3.5 Post-treatment response prediction

4.3.6 Post-treatment radionecrosis

4.4 Summary

Chapter 5 Applications of artificial intelligence in the fields of brain and prostate cancer

Abbreviations

5.1 Introduction

5.2 AI applications in brain cancer

5.2.1 Brain tumor segmentation

5.2.2 Survival prognosis

5.2.3 Surgical performance

5.3 AI applications in prostate cancer

5.3.1 Analyzing histopathological images.

5.3.2 PCa segmentation

5.3.3 Robotic surgery

5.3.4 PCa treatment

5.4 Conclusions

Acknowledgments

Chapter 6 AI-based non-deep learning and deep learning techniques used to accurately predict prostate cancer

6.1 Introduction

6.2 Study data

6.2.1 Dataset

6.3 AI-based non-deep-learning prediction methods

6.3.1 Handcrafted features

6.3.2 Classification algorithms

6.4 AI-based deep learning prediction methods

6.4.1 Convolutional neural network (CNN) overview

6.4.2 CNN methods

6.4.3 CNN layers

6.4.4 Training/testing data formulation

6.4.5 Performance evaluation measures

6.4.6 Receiver operating characteristic curve

6.5 Results and discussion

6.6 Conclusions and future recommendations

Chapter 7 Intelligent brain tumor classification using deep convolutional neural networks with transfer learning

7.1 Introduction

7.2 Materials and methods

7.2.1 MR images

7.2.2 Image analysis

7.2.3 Transfer learning

7.2.4 Data augmentation

7.2.5 Results

7.2.6 Discussion

7.3 Conclusions

Chapter 8 Big data applications in radiation oncology: challenges and opportunities

8.1 Introduction

8.2 Methods for structure set standardization

8.2.1 Overview

8.2.2 DICOM structure set standardization methods

8.2.3 Results

8.3 The use of natural language processing with medical texts

8.3.1 NLP feature extraction and models

8.3.2 NLP implementation results

8.3.3 Challenges for NLP in understanding free text

8.4 Standardization through structured templates

8.4.1 Manual data extraction

8.4.2 Analytic dashboard

8.4.3 Limitations of automated data extraction

8.4.4 Health Information Gateway Exchange (HINGE)

8.5 Future directions in data standardization and aggregation

8.5.1 Retrospective data.

8.5.2 Transfer learning

8.5.3 Federated learning

8.6 Conclusions

Chapter 9 A hybrid approach to the hyperspectral classification of in vivo brain tissue: linear unmixing with spatial coherence and machine learning

9.1 Introduction

9.2 Intraoperative HS acquisition system and HS dataset

9.2.1 Data preprocessing

9.3 Processing framework based on linear unmixing with spatial coherence and machine learning

9.3.1 Abundances estimation

9.3.2 End-members estimation

9.3.3 Internal abundances estimation

9.3.4 Machine learning for classification

9.4 Hybrid classification methodology

9.5 Experimental results and discussion

9.5.1 Evaluation of the hybrid classification methodology

9.5.2 Comparison with other related works

9.5.3 Limitations

9.6 Conclusions

Chapter 10 Application and post-hoc explainability of deep convolutional neural networks for bone cancer metastasis classification in prostate patients

10.1 Introduction

10.2 Computer-aided diagnosis (CAD) system

10.2.1 Study population

10.2.2 Explainable deep learning pipeline for diagnosis

10.3 Results

10.3.1 Bone metastasis classification results

10.3.2 Post-hoc explainability results

10.4 Discussion

10.5 Conclusions

Chapter 11 Prostate cancer detection using histopathology image analysis

11.1 Introduction

11.2 Histopathological images

11.3 Handcrafted feature-based CAD

11.4 Deep learning-based CAD

11.5 Conclusions

Chapter 12 Machine learning of gliomas in 3D dynamic contrast enhanced MRI: automatic segmentation and classification

12.1 Introduction

12.2 Segmentation and classification methods

12.2.1 Segmentation method

12.2.2 Automatic classification system

12.3 Results

12.3.1 Segmentation results.

12.3.2 Classification results

12.4 Discussion

12.4.1 Comparison of segmentation methods

12.4.2 Correlation thresholds and feature lists

12.4.3 Classification results using positive features

12.4.4 Significant radiomics features

12.4.5 Limitations

12.5 Conclusions

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

9780750345071

0750345071

OCLC:

1429724075