Artificial intelligence in cancer diagnosis and prognosis. Volume 1, Lung and kidney 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 (250 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

Chapter 1 American Joint Committee on Cancer staging of lung and renal cancers using a recurrent deep neural network model

1.1 Introduction

1.2 Background

1.2.1 Lung cancer

1.2.2 Renal cancer

1.2.3 Research scope

1.3 Methodology

1.3.1 AJCC staging

1.3.2 Database

1.3.3 The deep learning model

1.4 The experiment

1.5 Results and discussion

1.6 Conclusions

References

Chapter 2 Neural-ensemble-based detection: a modern way to diagnose lung cancer

2.1 Introduction

2.1.1 Lung cancer epidemiology

2.1.2 Signs and symptoms of lung cancer

2.1.3 Staging of lung cancer

2.1.4 Classification of lung cancer

2.2 Different methods of lung cancer detection

2.2.1 Invasive methods

2.2.2 Non-invasive methods

2.3 Neural-ensemble-based detection

2.4 Conclusions

References and further reading

Chapter 3 Computed tomography and magnetic resonance imaging machine learning applications for renal cell carcinoma

3.1 Background

3.2 Applications

3.2.1 Malignant versus benign discrimination

3.2.2 Malignancy subtyping

3.2.3 Biologic aggressiveness

3.2.4 Correlation with overall and progression-free survival under treatment

3.2.5 Prediction of perioperative complications

3.3 Conclusions

Chapter 4 Pulmonary nodule-based feature learning for automated lung tumor grading using convolutional neural networks

4.1 Introduction

4.2 Literature review

4.2.1 Preprocessing

4.2.2 Candidate nodule segmentation

4.2.3 Feature extraction and classification

4.3 Methodology

4.3.1 Data acquisition

4.3.2 Preprocessing

4.3.3 NROI segmentation

4.3.4 GAN

4.3.5 Feature extraction

4.3.6 Classification

4.4 Results and discussion.

4.5 Conclusions

Chapter 5 Detection of lung contours using closed principal curves and machine learning

5.1 Introduction

5.2 Materials and methods

5.2.1 Principal curve

5.2.2 Machine learning

5.2.3 Proposed algorithm

5.2.4 Quantitative evaluation

5.3 Results and discussion

5.3.1 Detecting contours in the private dataset using different learning rates

5.3.2 Detecting contours in the private dataset using different numbers of neurons in the hidden layer

5.3.3 Detecting contours in the private dataset using different numbers of epochs

5.3.4 Detecting contours in the private dataset using different algorithms

5.3.5 Detecting contours in the public LIDC-IDRI dataset using different algorithms

5.4 Conclusions

Acknowledgments

Chapter 6 Bytes, pixels, and bases: machine learning in imaging-omics for renal cell carcinoma

6.1 Introduction

6.1.1 The convergence of computers and cancer care

6.2 Imaging in renal cell carcinoma

6.2.1 Radiology

6.2.2 Pathology

6.3 Omics in renal cell carcinoma

6.3.1 Multiomics

6.4 Imaging-omics for kidney carcinoma

6.4.1 Radiomics

6.4.2 Pathomics

6.5 Opportunities and obstacles

6.5.1 Data

6.5.2 Interpretability

6.5.3 Privacy

6.5.4 Adversarial attacks

6.5.5 Regulatory roadblocks

6.6 Future directions

6.7 Conclusions

Chapter 7 Detection, growth quantification, and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans

7.1 Introduction

7.2 Background

7.2.1 Nodule detection

7.2.2 Nodule quantification

7.2.3 Lung cancer prediction

7.3 Temporal lung nodule assessment

7.3.1 Preprocessing

7.3.2 Nodule detection

7.3.3 Nodule reidentification

7.3.4 Nodule growth quantification

7.3.5 Nodule malignancy classification.

7.4 Data cohort

7.4.1 Scanners and protocols

7.4.2 Data

7.5 Results

7.5.1 Nodule detection

7.5.2 Nodule reidentification

7.5.3 Nodule growth quantification

7.5.4 Nodule malignancy classification

7.6 Discussion

7.7 Conclusions

Chapter 8 Training a deep multiview model using small samples of medical data

8.1 Introduction

8.2 Related work

8.2.1 Cox proportional hazard model

8.2.2 Deep survival models

8.3 Methodology

8.3.1 Training the deep multiview model on small numbers of data samples

8.3.2 Training the network using a divide-and-conquer strategy

8.3.3 Training the model as a multitask model (MM)

8.4 Experiments and discussion

8.4.1 Data set descriptions

8.4.2 Data preprocessing

8.4.3 Experimental setup

8.4.4 Results

8.4.5 Discussion

8.5 Conclusions

Chapter 9 Overview of deep learning for lung cancer diagnosis

9.1 Introduction

9.2 Deep learning

9.2.1 Convolutional neural networks

9.2.2 Transfer learning models

9.2.3 The U-Net

9.3 Evaluation criteria

9.3.1 Evaluation metrics used in classification applications

9.3.2 Evaluation metrics used in segmentation applications

9.4 Datasets

9.4.1 The LIDC-IDRI data set

9.4.2 The LungCT-Diagnosis data set

9.4.3 The NSCLC-Radiomics data set

9.5 Overview of recent research

9.6 Discussion

9.7 Conclusions

Chapter 10 Artificial intelligence for cancer diagnosis

10.1 Introduction

10.2 Background of cancer

10.3 The basics of artificial intelligence

10.4 AI impacts on cancer-based clinical analysis

10.5 Visualization tools for AI-assisted cancer recognition systems

10.6 Multi-platform deployment for cancer prognosis systems

10.7 Case studies of cancer recognition systems that use artificial intelligence techniques.

10.8 Conclusions

Chapter 11 Lung cancer diagnosis using 3D-CNN and spherical harmonics expansions

11.1 Introduction

11.2 Methodology

11.2.1 Feature extraction utilizing convolutional neural networks

11.2.2 Explanation of feature extraction utilizing spherical harmonics

11.3 Results

11.3.1 Experimental setup

11.3.2 Experimental evaluation

11.4 Conclusions

References.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

9780750345057

0750345055

OCLC:

1429724077