Pin Wang

TL;DR: An automatic quantitative image analysis technique of BCH images with top-bottom hat transform applied for nuclei segmentation and a double-strategy splitting model containing adaptive mathematical morphology and Curvature Scale Space corner detection method is applied to split overlapped cells for better accuracy and robustness.

TL;DR: The proposed segmentation and classification methods can automatically and effectively segment cell nuclei of microscopic images and the feature selection method based on CAGA with Gabor features has the highest classification performance for normal, uninvolved and abnormal images.

TL;DR: The proposed method FE-BkCapsNet, a novel structure with dual channels which can extract convolution features and capsule features simultaneously, integrate sematic features and spatial features into new capsules to obtain more discriminative information is designed.

TL;DR: A proposed PD classification algorithm that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm that can be applied to future studies seeking to improve PD classification methods is shown.

TL;DR: A hybrid structure which includes a double deep transfer learning (D2TL) and interactive cross-task extreme learning machine (ICELM) is proposed based on feature extraction and representation ability of CNN and classification robustness of ELM to provide an efficient tool for breast cancer classification in clinical settings.