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Proceedings Paper

Generative modeling for renal microanatomy
Author(s): Leema Krishna Murali; Brendon Lutnick; Brandon Ginley; John E. Tomaszewski; Pinaki Sarder
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Paper Abstract

Generative adversarial networks (GANs) have received immense attention in the field of machine learning for their potential to learn high-dimensional and real data distribution. These methods do not rely on any assumptions about the data distribution of the input sample and can generate real-like samples from latent vector space based on unsupervised learning. In the medical field, particularly, in digital pathology expert annotation and availability of a large set of training data is costly and the study of manifestations of various diseases is based on visual examination of stained slides. In clinical practice, various staining information is required to improve the pathological diagnosis process. But when the sampled tissue to be examined is limited, the final diagnosis made by the pathologist is based on limited stain styles. These limitations can be overcome by studying the usability and reliability of generative models in the field of digital pathology. To understand the usability of the generative models, we synthesize in an unsupervised manner, high resolution renal microanatomical structures like renal glomerulus in thin tissue histology images using state-of-art architectures like Deep Convolutional Generative Adversarial Network (DCGAN) and Enhanced Super- Resolution Generative Adversarial Network (ESRGAN). Successful generation of such structures will lead to obtaining a large set of labeled data for further developing supervised algorithms for disease classification and understanding progression. Our study suggests while GAN is able to attain formalin fixed and paraffin embedded tissue image quality, GAN requires further prior knowledge as input to model intrinsic micro-anatomical details, such as capillary wall, urinary pole, nuclei placement, suggesting developing semi-supervised architectures by using these above details as prior information. Also, the generative models can be used to create an artificial effect of staining without physically tampering the histopathological slide. To demonstrate this, we use a CycleGAN network to transform Hematoxylin and eosin (H&E) stain to Periodic acid-Schiff (PAS) stain and Jones methenamine silver (JMS) stain to PAS stain. In this way GAN can be employed to translate different renal pathology stain styles when the relevant staining information is not available in the clinical settings.

Paper Details

Date Published: 16 March 2020
PDF: 10 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200F (16 March 2020); doi: 10.1117/12.2549891
Show Author Affiliations
Leema Krishna Murali, The State Univ. of New York at Buffalo (United States)
Brendon Lutnick, The State Univ. of New York at Buffalo (United States)
Brandon Ginley, The State Univ. of New York at Buffalo (United States)
John E. Tomaszewski, The State Univ. of New York at Buffalo (United States)
Pinaki Sarder, The State Univ. of New York at Buffalo (United States)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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