Computational Pathology

Foundation models enable clinical-grade computational pathology across diverse cancer types, including rare cancers. Conventional computational pathology systems rely on predetermined analytical pathways. Tissue-level and single-cell feature extraction with machine learning can generate clinically actionable diagnostic and prognostic information. Prior computational pathology approaches established that tissue-level and single-cell feature extraction with machine learning can generate clinically actionable diagnostic and prognostic information. Tissue-level and single-cell feature extraction combined with machine learning can generate clinically actionable diagnostic and prognostic information. Combining tissue-level and single-cell feature extraction with machine learning can generate clinically actionable diagnostic and prognostic information. Both tissue-level and single-cell feature extraction combined with machine learning can generate clinically actionable diagnostic and prognostic information. Digital pathology quickly showed enough reliability to become part of routine clinical care. Prior AI approaches combining tissue-level and single-cell feature extraction with machine…