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Histopathological images contain abundant phenotypic information andpathological patterns, which are the gold standards for disease diagnosis andessential for the prediction of patient prognosis and treatment outcome. Inrecent years, computer-automated analysis techniques for histopathologicalimages have been urgently required in clinical practice, and deep learningmethods represented by convolutional neural networks have gradually become themainstream in the field of digital pathology. However, obtaining large numbersof fine-grained annotated data in this field is a very expensive and difficulttask, which hinders the further development of traditional supervisedalgorithms based on large numbers of annotated data. More recent studies havestarted to liberate from the traditional supervised paradigm, and the mostrepresentative ones are the studies on weakly supervised learning paradigmbased on weak annotation, semi-supervised learning paradigm based on limitedannotation, and self-supervised learning paradigm based on pathological imagerepresentation learning. These new methods have led a new wave of automaticpathological image diagnosis and analysis targeted at annotation efficiency.With a survey of over 130 papers, we present a comprehensive and systematicreview of the latest studies on weakly supervised learning, semi-supervisedlearning, and self-supervised learning in the field of computational pathologyfrom both technical and methodological perspectives. Finally, we present thekey challenges and future trends for these techniques.

physics in medicine and biology/physics in medicine and biology

Towards Label-efficient Automatic Diagnosis and Analysis: A  Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised,  Semi-supervised and Self-supervised Techniques in Histopathological Image  Analysis