Deep Learning in Healthcare: Paradigms and Applications

Medical imaging is a demanding proving ground for deep learning: models must detect, classify, segment, and enhance images while addressing practical constraints such as limited labeled data. This edited reference examines those challenges through foundational methods, specialized research approaches, and a clinical imaging application.

Deep Learning in Healthcare: Paradigms and Applications brings together work on how neural-network methods are being applied to medical image analysis and computer-aided diagnosis. Its three-part structure helps readers move from core concepts toward advanced techniques and concrete healthcare examples.

From image fundamentals to healthcare use

The opening section introduces deep-learning methods for medical image detection, segmentation, classification, and enhancement. Together, these topics show how distinct image-analysis tasks fit within a broader clinical and computational landscape.

Research challenges, examined in context

The advanced section turns to focused problems: improving segmentation when training resources are limited; reducing annotation effort through active and self-paced learning; and analyzing medical-image texture. Other chapters address structural MRI for Alzheimer’s disease diagnosis, emphysema classification and quantification in CT, diffuse lung disease opacity labeling, and unsupervised feature learning for HEp-2 cell staining patterns.

A clinical imaging application

The final part presents Dr. Pecker, a deep-learning-based computer-aided diagnosis system for medical imaging. This case study gives the collection a practical endpoint after its coverage of methods and research problems.

For readers across AI and medicine

This volume may interest computer science and engineering students and researchers, medical professionals, and others exploring deep learning in healthcare. Its value lies in bringing broad technical themes together with specific imaging applications, offering a reference point for readers who want to understand both the methods and the kinds of problems they are used to investigate.