Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images

Kini, Jyoti R and Lal, Shyam and *, Raghavendra BS and *, Das D and *, Ravi A and *, Kumar A and *, Chanchal AK and *, Yatgiri RP and *, Aatresh AA (2021) Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images. Computerized Medical Imaging and Graphics, 93. pp. 1-15. ISSN 08956111

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Abstract

Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet

Item Type: Article
Uncontrolled Keywords: Deep learning, Dimension-wise convolutions, Convolutional neural networks, Nuclei segmentation
Subjects: Medicine > KMC Mangalore > Pathology
Depositing User: KMCMLR User
Date Deposited: 17 Dec 2021 11:03
Last Modified: 17 Dec 2021 11:03
URI: http://eprints.manipal.edu/id/eprint/157730

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