Blind deblurring for microscopic pathology images using deep learning networks

• URL: http://arxiv.org/abs/2011.11879v1
• Date: Tue, 24 Nov 2020 03:52:45 GMT
• Title: Blind deblurring for microscopic pathology images using deep learning networks
• Authors: Cheng Jiang (1), Jun Liao (1), Pei Dong (1), Zhaoxuan Ma (1), De Cai (1), Guoan Zheng (2), Yueping Liu (3), Hong Bu (4 and 5) and Jianhua Yao (1) ((1) Tencent AI Lab, Shenzhen, China,(2) Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA,(3) Department of Pathology, The Fourth Hospital of Hebei Medical University, Hebei, China,(4) Department of Pathology, West China Hospital, Sichuan University, Chengdu, China,(5) Laboratory of Pathology, Clinical Research Centre for Breast, West China Hospital, Sichuan University, Chengdu, China.)
• Abstract summary: We demonstrate a deep-learning-based approach that can alleviate the defocus and motion blur of a microscopic image. It produces a sharper and cleaner image with retrieved fine details without prior knowledge of the blur type, blur extent and pathological stain. We test our approach on different types of pathology specimens and demonstrate great performance on image blur correction and the subsequent improvement on the diagnosis outcome of AI algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency. However, defocus and motion blur can obscure tissue or cell characteristics hence compromising AI algorithms'accuracy and robustness in analyzing the images. In this paper, we demonstrate a deep-learning-based approach that can alleviate the defocus and motion blur of a microscopic image and output a sharper and cleaner image with retrieved fine details without prior knowledge of the blur type, blur extent and pathological stain. In this approach, a deep learning classifier is first trained to identify the image blur type. Then, two encoder-decoder networks are trained and used alone or in combination to deblur the input image. It is an end-to-end approach and introduces no corrugated artifacts as traditional blind deconvolution methods do. We test our approach on different types of pathology specimens and demonstrate great performance on image blur correction and the subsequent improvement on the diagnosis outcome of AI algorithms.

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