About this Research Topic
The huge amount of information and data available in histopathology images, and the ease of their digitization has rapidly advanced the field of computational pathology. The effectiveness of computational pathology, coupled with the impressive advances that the fields of deep learning and computer vision have made in recent years, make for the perfect combination to revolutionize established workflows in research and clinic.
The goal of this Research Topic is to publish the latest research advances and bring together scientific researchers, medical experts and industry partners working in the field of computational pathology for clinical outcome analysis. We welcome papers that cover a wide spectrum of image analysis techniques for semi- or fully automated analysis of computational histopathology images. Topics will include (but are not limited to) machine learning methods and deep learning with their applications to:
● Image analysis of anatomical structures/functions and lesions
● Deep Learning for Computational Pathology
● Domain adaptation and transfer learning
● Computer-aided detection/diagnosis
● Image segmentation
● Stain normalization/standardization
● Multi-modality fusion for analysis, diagnosis, and intervention;
● Medical image reconstruction;
● Medical image retrieval;
● Molecular/pathologic/cellular image analysis;
● Dynamic, functional, and physiologic imaging.
● Detection of predictive and prognostic tissue biomarkers
● Whole-slide image analysis
● Registration of whole-slide images
● Immunohistochemistry scoring
● Multiplexed staining
● Unlabeled multiplexing
● Crowdsourcing for machine learning applications
● Applications of computational pathology in the clinic
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.