Artificial Intelligence and Deep Learning in Pathology

Focusing on the application of computational methods to image-based medical diagnostics. The book, edited by Stanley Cohen, M.D., covers fundamental machine learning concepts, including shallow learning algorithms like K-Means and k-Nearest Neighbors, alongside complex deep learning architectures such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). A significant portion of the material details the integration of Whole Slide Imaging (WSI) and AI in anatomic pathology to improve diagnosis, grading, and prognosis, often requiring large, well-annotated datasets. Furthermore, the text addresses critical practical issues like data preprocessing, mitigating bias, regulatory concerns, and the future role of AI as a digital assistant for pathologists, particularly in complex tasks like identifying tumor-infiltrating lymphocytes (TILs) and assessing molecular markers.

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