Artificial Intelligence in Cancer Diagnosis and Prognosis, Volume 2: Breast and bladder cancer

PRELIMS.pdf
	Preface
	Acknowledgements
	Editor biographies
		Ayman El-Baz
		Jasjit S Suri
	List of contributors
		Outline placeholder
			Abdullah-Al Nahid
			Fatma Anwar
			Omneya Attallah
			Bensujin Bennet
			Yin Dai
			Fei Gao
			Yuliana Jiménez Gaona
			Nagia M Ghanem
			Agus Rizal A H Hamid
			Tao Huang
			Mohamed A Ismail
			Shomona Gracia Jacob
			R Karthiga
			Elliot S Kim
			Valentina L Kouznetsova
			Vasudevan Lakshminarayanan
			Yang Lei
			Chen Li
			Xiaoyan Li
			Tian Liu
			Darwin Castillo Malla
			Shadab Momin
			K Narasimhan
			Anindya Pradipta
			Keerthana Prasad
			Saifur Rahman Sabuj
			Johir Raihan
			N Raju
			R Rashmi
			María José Rodríguez-Álvarez
			Niloy Sikder
			Mashiro Sugimoto
			Shenghua Tian
			Igor F Tsigelny
			Chethana Babu K Udupa
			Changchun Yang
			Xiaofeng Yang
			Prasandhya A Yusuf
			Haiqing Zhang
			Yuchao Zheng
			Xiaomin Zhou
CH001.pdf
	Chapter 1 Development of artificial neural networks for breast histopathological image analysis
		1.1 Introduction
			1.1.1 General development of the current AI analysis of histopathology
		1.2 BHIA using classical ANNs
			1.2.1 Related works
			1.2.2 Summary
		1.3 BHIA using deep neural networks
			1.3.1 Related works
			1.3.2 Summary
		1.4 Methodological analysis
			1.4.1 Analysis of classical ANN methods
			1.4.2 Analysis of deep ANN methods
		1.5 Conclusions
		Acknowledgements
		References
CH002.pdf
	Chapter 2 Machine learning in bladder cancer diagnosis
		2.1 Introduction
			2.1.1 Previous metabolomic studies of BCa detection
		2.2 The approach (overview)
			2.2.1 MetaboAnalyst
			2.2.2 Ingenuity® pathway analysis
			2.2.3 Venny
			2.2.4 Analysis via machine learning
			2.2.5 Datasets
		2.3 Results
			2.3.1 BCa metabolic pathways
			2.3.2 Integrative analysis of the networks
		2.4 Feedback loops that include genes and metabolites
		2.5 Machine learning classifiers
		2.6 Conclusions
		References
CH003.pdf
	Chapter 3 Deep learning in photoacoustic breast cancer imaging
		3.1 Introduction
			3.1.1 Photoacoustic imaging
			3.1.2 Deep learning
		3.2 Deep learning in photoacoustic breast cancer imaging
			3.2.1 Data synthesis
			3.2.2 Semantic image annotation
		3.3 Conclusions and outlook
		References
CH004.pdf
	Chapter 4 Histopathological breast cancer image classification with feature prioritization using a heuristic algorithm
		4.1 Introduction
		4.2 Methodology
			4.2.1 Feature extraction
			4.2.2 Classifier tools
			4.2.3 Feature selection
			4.2.4 Performance metrics
		4.3 Results for extracted features
			4.3.1 On separate datasets
			4.3.2 Ensemble
		4.4 Feature selection
			4.4.1 Genetic-algorithm-based feature selection
			4.4.2 Results for selected features
		4.5 Comparison with other findings
		4.6 Conclusions
		References
CH005.pdf
	Chapter 5 The use of machine learning and biofluid metabolomics in breast cancer diagnosis
		5.1 Introduction
		5.2 Metabolomics
			5.2.1 Biomarkers for breast cancer diagnosis
		5.3 Diagnosis method using multiple markers
			5.3.1 Quality control of measurement and biomarker discoveries
			5.3.2 Multivariate analysis for classification
			5.3.3 Artificial intelligence-based approach
		5.4 Discussion
		5.5 Conclusions
		References
CH006.pdf
	Chapter 6 AUTO-BREAST: a fully automated pipeline for breast cancer diagnosis using AI technology
		6.1 Introduction
		6.2 Methods
			6.2.1 Datasets
			6.2.2 Proposed AUTO-BREAST pipeline
		6.3 Assessment measures
		6.4 Results
			6.4.1 Scenario I results
			6.4.2 Scenario II results
			6.4.3 Scenario III results
		6.5 Discussion
		6.6 Conclusions
		References
CH007.pdf
	Chapter 7 Diagnosis of breast cancer from histopathological images using artificial intelligence
		7.1 Background of artificial intelligence
		7.2 Introduction to breast cancer
			7.2.1 Histopathology
			7.2.2 Histology tissue preparation procedures
			7.2.3 Scarff–Bloom–Richardson (SBR) grading
			7.2.4 Types of breast cancer
		7.3 Application of AI to histopathological image analysis
			7.3.1 Motivation
			7.3.2 Datasets
			7.3.3 Classification of AI methods
		7.4 Discussion
		References and further reading
CH008.pdf
	Chapter 8 The role of artificial intelligence in the field of bladder cancer
		8.1 Introduction
		8.2 The unmet need in BCa management and how AI can help
			8.2.1 Urinary cytology detection
			8.2.2 Cystoscopy detection
			8.2.3 Histopathology detection
			8.2.4 Radiology detection
			8.2.5 BCa predictive evaluation
		8.3 The challenge of AI implementation in BCa management
		8.4 Conclusions
		Acknowledgment
		References
CH009.pdf
	Chapter 9 Exploring data science paradigms in breast cancer classification: linking data, learning, and artificial intelligence in medical diagnosis
		9.1 Introduction
		9.2 Data management for machine learning
			9.2.1 Issues in data management for ML and AI
			9.2.2 Data corroboration
			9.2.3 Data correction
			9.2.4 Data amelioration
		9.3 Machine learning in breast cancer research
		9.4 Computational approaches: challenges and achievements in healthcare
		9.5 AI tools used in breast cancer research
		9.6 Conclusions and the future of AI in healthcare
		References and further reading
CH010.pdf
	Chapter 10 Automatic detection and classification of invasive ductal carcinoma in histopathology images using convolutional neural networks
		10.1 Introduction
			10.1.1 Mammography
			10.1.2 Digital breast tomosynthesis
			10.1.3 Contrast-enhanced digital mammogram
			10.1.4 Sonography
			10.1.5 Automated breast ultrasound
			10.1.6 Doppler techniques
			10.1.7 Sonoelastography
			10.1.8 Magnetic resonance imaging
			10.1.9 Optical imaging
			10.1.10 Microwave imaging
			10.1.11 Thermal imaging
			10.1.12 Histopathology
			10.1.13 Artificial intelligence
			10.1.14 Machine learning
			10.1.15 Deep learning
		10.2 Literature survey
		10.3 Methodology
			10.3.1 Dataset
			10.3.2 Proposed work
			10.3.3 Results and discussion
		10.4 Conclusions
		References
CH011.pdf
	Chapter 11 Machine learning analysis of breast cancer single-cell omics data
		11.1 Introduction
		11.2 Single-cell data analysis
			11.2.1 Imputation of gene expression
			11.2.2 Cell clustering
			11.2.3 Identification of marker genes
			11.2.4 Regulatory network analysis
			11.2.5 Spatial transcriptomics
			11.2.6 scATAC
			11.2.7 scDNA methylation
			11.2.8 Single-cell exome
			11.2.9 Single-cell metabolome
			11.2.10 Integrated analysis
		11.3 Breast cancer single-cell studies
		11.4 Outlook
		References
CH012.pdf
	Chapter 12 Radiomics, deep learning, and breast cancer detection
		12.1 Introduction
			12.1.1 Imaging modalities used for breast cancer
		12.2 Overview
			12.2.1 Literature review
			12.2.2 Blibliometric analysis
			12.2.3 Imaging modalities and databases
			12.2.4 Morphological features
			12.2.5 Deep learning methods
			12.2.6 Deep learning CAD system steps
		12.3 Results
			12.3.1 Databases
			12.3.2 Deep learning architectures and performance metrics
		12.4 Discussion and conclusions
		References and further reading
CH013.pdf
	Chapter 13 Artificial-intelligence-based techniques for the diagnosis of bladder and breast cancer
		13.1 Introduction
		13.2 Method
			13.2.1 Article search and selection
		13.3 Results
			13.3.1 Traditional ML methods
			13.3.2 DL methods
		13.4 Discussion and conclusions
		References