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20112026
most citedLatent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities

221 citations · 345 across the 16 of their papers we have counts for

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cs.CV2020

Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling

Fabian Dubourvieux, Romaric Audigier, Angelique Loesch +2

Person Re-Identification (re-ID) aims at retrieving images of the same person taken by different cameras. A challenge for re-ID is the performance preservation when a model is used…

eess.IV2020

A review: Deep learning for medical image segmentation using multi-modality fusion

Tongxue Zhou, Su Ruan, Stéphane Canu

Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusi…

eess.IV2020

An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism

Tongxue Zhou, Stéphane Canu, Su Ruan

The coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health. Computed Tomography (CT) is an effective tool in the screening of COVID-19.…

eess.IV2020

Brain tumor segmentation with missing modalities via latent multi-source correlation representation

Tongxue Zhou, Stéphane Canu, Pierre Vera +1

Multimodal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to have missing imaging modalities in clinical practice. Sinc…

math.OC2020

Learning Discontinuous Piecewise Affine Fitting Functions using Mixed Integer Programming for Segmentation and Denoising

Ruobing Shen, Bo Tang, Leo Liberti +2

Piecewise affine functions are widely used to approximate nonlinear and discontinuous functions. However, most, if not all existing models only deal with fitting continuous functio…