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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…
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.…
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images
Amine Amyar, Su Ruan, Pierre Vera +2
One of the most challenges in medical imaging is the lack of data. It is proven that classical data augmentation methods are useful but still limited due to the huge variation in i…
Weakly Supervised PET Tumor Detection Using Class Response
Amine Amyar, Romain Modzelewski, Pierre Vera +2
One of the most challenges in medical imaging is the lack of data and annotated data. It is proven that classical segmentation methods such as U-NET are useful but still limited du…
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…