Qi Zhang

TL;DR: Dual-modal features can be extracted from RTE and B-mode ultrasound with computer assistance and are valuable for discrimination between benign and metastatic lymph nodes, which could be potentially used in daily clinical practice for assessing axillary metastasis in breast cancer patients.

TL;DR: A computer-aided method is proposed for objective and convenient quantification of contrast agent spatial distribution within plaques in CEUS image sequences including cardiac cycle retrieval and sub-sequence selection, temporal mean image segmentation, and texture feature extraction.

TL;DR: A CEUS-based computer-aided diagnosis for liver cancers with only three typical CEUS images selected from three phases is proposed, which simulates the clinical diagnosis mode of radiologists.

TL;DR: A quaternion-based GANet (QGANet) algorithm is further developed to learn effective feature representations containing color information for histopathological images, which indicates that the proposed QGANet achieves the best performance on the classification of color histopathology images among all the compared algorithms.

TL;DR: The experimental results demonstrate that the proposed RBM+ works well as an LUPI algorithm for feature learning, and the ensemble L UPI algorithm is superior to the traditional predictive models for the MRI-based AD diagnosis using the positron emission tomography as the privileged information.