Artificial Intelligence in Cancer Diagnosis and Prognosis: Breast and bladder cancer (Volume 2) (Physics and Engineering in Medicine and Biology, Volume 2) (Original Publisher)

Part of the Physics and Engineering in Medicine and Biology series, Artificial Intelligence in Cancer Diagnosis and Prognosis: Breast and Bladder Cancer (Volume 2) (1st Edition, 2022) delves into the practical and theoretical advancements of artificial intelligence in cancer detection, classification, and prognostic modeling. With the global burden of breast and bladder cancer continuing to rise, this volume highlights how AI technologies—particularly machine learning, deep learning, and computer-aided diagnostics—are being applied to enhance clinical accuracy, streamline workflows, and improve patient outcomes.

Target Audience:
This volume is tailored for:

• Biomedical engineers and medical physicists developing diagnostic tools
• Oncologists, radiologists, and urologists integrating AI into clinical practice
• Healthcare data scientists and AI researchers in cancer informatics
• Graduate students and academic professionals exploring the intersection of AI and oncology

Key Features and Highlights:

• Focuses specifically on AI applications in breast and bladder cancer diagnosis and prognosis
• Discusses machine learning algorithms, neural networks, and image analysis models
• Covers multi-modal data integration from imaging, pathology, and clinical variables
• Explores the role of predictive analytics in patient risk stratification and outcome forecasting
• Includes insights into regulatory and ethical considerations in AI deployment in oncology
• Authored by multidisciplinary experts from the fields of medical physics, AI, and clinical oncology

Topics Covered:
Includes core chapters on:

• AI-based diagnostic frameworks for breast and bladder cancer
• Image recognition and pattern detection using deep learning
• Integration of histopathological, radiological, and genomic data
• Prognostic modeling and survival prediction using AI
• Challenges in clinical validation and real-world implementation

About the Author:
This volume is authored and edited by a team of international experts in medical physics, artificial intelligence, and oncology research, contributing cutting-edge findings from both academic and clinical settings.

File Format & Technical Specifications:

• Available Formats: PDF
• File Size: Approximately 8–12 MB
• Language: English
• Device Compatibility: Works on all major e-readers, tablets, laptops, and smartphones that support PDF

FAQs:

1. Does the book include real clinical datasets or case studies?
Yes, several chapters reference clinical imaging datasets and include case-based discussions to illustrate real-world applications of AI in breast and bladder cancer diagnosis.

2. Is this volume suitable for someone without a programming background?
While technical in nature, the book offers conceptual overviews alongside algorithmic details, making it accessible to clinicians and healthcare professionals interested in AI without requiring coding expertise.