ALL-IDB : the acute lymphoblastic leukemia image database for image processing

The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and increase the accuracy and homogeneity of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper, we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of the cells is given, as well as a specific set of figures of merits to fairly compare the performances of different algorithms. This initiative aims to offer a new test tool to the image processing and pattern matching communities, direct to stimulating new studies in this important field of research

PLS-Based Gene Selection and Identification of Tumor-Specific Genes

National Natural Science Foundation of China [60774033]; Special Research Fund for the Doctoral Program of Higher Education [20070384003, 20090121110022]; Xiamen University [0630-E62000]; Natural Sciences and Engineering Research Council of CanadaIn view of the characteristics of high-dimensional small sample, strong relevance, and high noise of the identification of tumor-specific genes on microarray, a novel partial least squares (PLS) based gene-selection method, which synthesizes genetic relatedness and is suitable for multicategory classification, is presented. Using the explanation difference of independent variables on dependent variable (class), we define three indicators for global gene selection, which takes into accounts the combined effects of all the genes and the correlation among the genes. Integrated with the linear kernel support vector classifier (SVC), the proposed method is tested by MIT acute myeloid leukemia/acute lymphoblastic leukemia (AML/ALL) and small round blue cell tumors (SRBCT) data sets. A subset of specific genes with small numbers and high identification are obtained. The results indicate that our proposed PLS-based method for tumor-specific genes selection is highly efficient. Compared to the literature, the selected specific genes from both two-category dataset AML/ALL and multicategory dataset SRBCT are credible. Further investigation shows that the proposed gene-selection method is robust. Overall, the proposed method can effectively solve feature-selection problem on high-dimensional small sample. At the same time, it has good performance for multicategory classification as well