Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging

Hyperspectral imaging (HSI) offers both spatial and spectral data across numerous contiguous wavelength bands, demonstrating unparalleled sensitivity in detecting small biochemical and morphological variations in biological tissues. The information content surpassing traditional visual imaging has generated novel potential in cancer diagnostics, histology, ophthalmology, endoscopy, and precision surgery. Despite its potential, the complete realization of HSI in medicine remains unfulfilled due to the complexity and high dimensionality of the data, obstacles posed by noise and variability, and the absence of standardized computing methodologies. Using deep learning techniques grounded in convolutional neural networks, recurrent and attention-based architectures, generative models, and multimodal fusion strategies may directly tackle these challenges. Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging explores the nascent field at the convergence of deep learning and HSI aimed at enhancing biological science and clinical practice. It examines computational techniques, applications in oncology, ophthalmology, gastroenterology, microbiology, and pathology, and future perspectives on real-time implementation, portability, ethics, and regulatory approval. This book covers topics such as disease detection, medical technologies, and anomaly detection, and is a useful resource for medical and healthcare professionals, engineers, academicians, researchers, and scientists.