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20182021
most citedDomain adaptation based self-correction model for COVID-19 infection segmentation in CT images

62 citations · 82 across the 3 of their papers we have counts for

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7 papers · 1 filter

cs.CV2020

Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy

Miaohua Zhang, Yongsheng Gao, Changming Sun +1

Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are…

cs.CV2020

A Robust Matching Pursuit Algorithm Using Information Theoretic Learning

Miaohua Zhang, Yongsheng Gao, Changming Sun +1

Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are b…

cs.CV2020

DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data

Wei Yin, Xinlong Wang, Chunhua Shen +5

We present a method for depth estimation with monocular images, which can predict high-quality depth on diverse scenes up to an affine transformation, thus preserving accurate shap…

cs.CV2019

Deep Learning based HEp-2 Image Classification: A Comprehensive Review

Saimunur Rahman, Lei Wang, Changming Sun +1

Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 c…

cs.CV201920 cited

Knowledge Adaptation for Efficient Semantic Segmentation

Tong He, Chunhua Shen, Zhi Tian +3

Both accuracy and efficiency are of significant importance to the task of semantic segmentation. Existing deep FCNs suffer from heavy computations due to a series of high-resolutio…

cs.CV2018

RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

Qiangguo Jin, Zhaopeng Meng, Changming Sun +2

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep convolutional neural network…