A review of machine learning approaches, challenges and prospects for computational tumor pathology. (arXiv:2206.01728v1 [eess.IV])

Computational pathology is part of precision oncology medicine. The
integration of high-throughput data including genomics, transcriptomics,
proteomics, metabolomics, pathomics, and radiomics into clinical practice
improves cancer treatment plans, treatment cycles, and cure rates, and helps
doctors open up innovative approaches to patient prognosis. In the past decade,
rapid advances in artificial intelligence, chip design and manufacturing, and
mobile computing have facilitated research in computational pathology and have
the potential to provide better-integrated solutions for whole-slide images,
multi-omics data, and clinical informatics. However, tumor computational
pathology now brings some challenges to the application of tumour screening,
diagnosis and prognosis in terms of data integration, hardware processing,
network sharing bandwidth and machine learning technology. This review
investigates image preprocessing methods in computational pathology from a
pathological and technical perspective, machine learning-based methods, and
applications of computational pathology in breast, colon, prostate, lung, and
various tumour disease scenarios. Finally, the challenges and prospects of
machine learning in computational pathology applications are discussed.

Source: https://arxiv.org/abs/2206.01728


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