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This document discusses the advancements in digital pathology through deep learning techniques, particularly focusing on the integration of graph neural networks (GNNs) to enhance cancer diagnosis. It highlights the potential of deep learning to automate and improve the accuracy of pathological diagnoses, addressing challenges faced by traditional methods. The paper reviews various deep learning models and their applications in differentiating between benign and malignant tumors, showcasing the superior per

Author

Canales Campos Anette Michel

Language

EN