Tianyang Li

TL;DR: This paper proposes an attention-based deep 3D multiple instance learning (AD3D-MIL) where a patient-level label is assigned to a 3D chest CT that is viewed as a bag of instances, which can semantically generate deep3D instances following the possible infection area.

TL;DR: The solution, discriminative cost-sensitive learning (DCSL), is reported, which should be the choice if the clinical needs the assisted screening of COVID-19 from chest X-rays, and is so flexible that it can apply in any deep neural network.

TL;DR: Wang et al. as discussed by the authors proposed an adaptive GPR denoising method based on the fast independent component analysis (FastICA) with wavelet transform modulus maxima (WTMM) multifractal spectrum, which can effectively separate the information of the abnormal body in the reservoir that is submerged by the noise signal.

TL;DR: This study proposes a cardiac cycle feature learning architecture for achieving an accurate and reliable estimation of the left ventricular ejection fraction, and demonstrates great potential for future clinical applications.

TL;DR: This work has developed and validated a relatively complete solution for the simultaneous 3D semantic segmentation of multiple spinal structures at the voxel level named as the S3egANet, and presented a multi-stage adversarial learning strategy to achieve high accuracy and reliability segmentsation of several spinal structures simultaneously.