Hospital-Agnostic Image Representation Learning in Digital Pathology. (arXiv:2204.02404v1 [eess.IV])

Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer
subtypes. The difference in procedures to acquire WSIs at various trial sites
gives rise to variability in the histopathology images, thus making consistent
diagnosis challenging. These differences may stem from variability in image
acquisition through multi-vendor scanners, variable acquisition parameters, and
differences in staining procedure; as well, patient demographics may bias the
glass slide batches before image acquisition. These variabilities are assumed
to cause a domain shift in the images of different hospitals. It is crucial to
overcome this domain shift because an ideal machine-learning model must be able
to work on the diverse sources of images, independent of the acquisition
center. A domain generalization technique is leveraged in this study to improve
the generalization capability of a Deep Neural Network (DNN), to an unseen
histopathology image set (i.e., from an unseen hospital/trial site) in the
presence of domain shift. According to experimental results, the conventional
supervised-learning regime generalizes poorly to data collected from different
hospitals. However, the proposed hospital-agnostic learning can improve the
generalization considering the low-dimensional latent space representation
visualization, and classification accuracy results.



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