Artificial Intelligence in Cancer Diagnosis and Prognosis, Volume 1: Lung and kidney cancer

PRELIMS.pdf
	Preface
	Acknowledgements
	Editor biographies
		Ayman El-Baz
		Jasjit S Suri
	List of contributors
CH001.pdf
	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
CH002.pdf
	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
CH003.pdf
	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
		References
CH004.pdf
	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
		References
CH005.pdf
	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
		References
CH006.pdf
	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
		References
CH007.pdf
	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
		References and further reading
CH008.pdf
	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
		References
CH009.pdf
	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
		References
CH010.pdf
	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
		References and further reading
CH011.pdf
	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