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Deep Neural Network With Consistency Regularization of Multi-Output Channels for Improved Tumor Detection and Delineation | IEEE Journals & Magazine | IEEE Xplore

Deep Neural Network With Consistency Regularization of Multi-Output Channels for Improved Tumor Detection and Delineation


Abstract:

Deep learning is becoming an indispensable tool for imaging applications, such as image segmentation, classification, and detection. In this work, we reformulate a standa...Show More

Abstract:

Deep learning is becoming an indispensable tool for imaging applications, such as image segmentation, classification, and detection. In this work, we reformulate a standard deep learning problem into a new neural network architecture with multi-output channels, which reflects different facets of the objective, and apply the deep neural network to improve the performance of image segmentation. By adding one or more interrelated auxiliary-output channels, we impose an effective consistency regularization for the main task of pixelated classification (i.e., image segmentation). Specifically, multi-output-channel consistency regularization is realized by residual learning via additive paths that connect main-output channel and auxiliary-output channels in the network. The method is evaluated on the detection and delineation of lung and liver tumors with public data. The results clearly show that multi-output-channel consistency implemented by residual learning improves the standard deep neural network. The proposed framework is quite broad and should find widespread applications in various deep learning problems.
Published in: IEEE Transactions on Medical Imaging ( Volume: 40, Issue: 12, December 2021)
Page(s): 3369 - 3378
Date of Publication: 28 May 2021

ISSN Information:

PubMed ID: 34048339

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