Xianling Hu

Bio: Xianling Hu is an academic researcher from Chongqing University. The author has contributed to research in topics: Mathematical morphology & Segmentation. The author has an hindex of 4, co-authored 5 publications receiving 231 citations. Previous affiliations of Xianling Hu include Third Military Medical University.

Proportional Hybrid Mechanism for Population Based Feature Selection Algorithm

Abstract: Feature selection is an important research field for pattern classification, data mining, etc. Population-based optimization algorithms (POA) have high parallelism and are widely used as search algorithm for feature selection. Population-based feature selection algorithms (PFSA) involve compromise between precision and time cost. In order to optimize the PFSA, the feature selection models need to be improved. Feature selection algorithms broadly fall into two categories: the filter model and the wrapper model. The filter model is fast but less precise; while the wrapper model is more precise but generally computationally more intensive. In this paper, we proposed a new mechanism — proportional hybrid mechanism (PHM) to combine the advantages of filter and wrapper models. The mechanism can be applied in PFSA to improve their performance. Genetic algorithm (GA) has been applied in many kinds of feature selection problems as search algorithm because of its high efficiency and implicit parallelism. Therefore, GAs are used in this paper. In order to validate the mechanism, seven datasets from university of California Irvine (UCI) database and artificial toy datasets are tested. The experiments are carried out for different GAs, classifiers, and evaluation criteria, the results show that with the introduction of PHM, the GA-based feature selection algorithm can be improved in both time cost and classification accuracy. Moreover, the comparison of GA-based, PSO-based and some other feature selection algorithms demonstrate that the PHM can be used in other population-based feature selection algorithms and obtain satisfying results.

Mammary glandular cell segmentation method based on multi-scale growth and double-strategy adhesion-removing model

Abstract: The invention discloses a mammary glandular cell segmentation method based on a multi-scale growth and double-strategy adhesion-removing model. The method comprises the following steps: firstly, inputting a mammary glandular tissue image and converting the image into a gray image; secondly, enhancing the contrast ratio; thirdly, carrying out cell positioning by using wavelet decomposition; fourthly, carrying out multi-scale region growth; fifthly, realizing primary segmentation of a cell region through voting and selecting; sixthly, judging whether the segmented region has cell adhesion or not; if the cell adhesion does not exist, determining that the segmented region is a single cell region, and outputting a segmentation result; if the cell adhesion exists, determining that the segmented region is an adhesion region, and carrying out adherent cell segmentation; and finally, carrying out adherent cell segmentation by using the double-strategy adhesion-removing model constructed by morphological corrosion-expansion operation and a corner detection segmentation algorithm until all the cells are segmented. By virtue of the method, the influences on mammary glandular cell segmentation, caused by a complicated background of a mammary glandular tissue slice image, are effectively inhibited; and the identification precision of an adherent cell segmentation line is improved and the segmentation precision of the adherent cells is further improved.

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model.

Abstract: Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.

Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map

Abstract: The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the development of interpretable models, the detection and segmentation of cell nuclei is of the utmost importance. In this paper, we describe a new method to automatically segment nuclei from Haematoxylin and Eosin (H&E) stained histopathology data with fully convolutional networks. In particular, we address the problem of segmenting touching nuclei by formulating the segmentation problem as a regression task of the distance map. We demonstrate superior performance of this approach as compared to other approaches using Convolutional Neural Networks.