The antinuclear antibody detection with human epithelial cells is a popular approach for autoimmune diseases diagnosis. The manual evaluation demands time, effort and capital, and automation in screening can greatly aid the physicians in these respects. In this work, we employ simple, efficient and visually more interpretable, class-specific features which defined based on the visual characteristics of each class. We believe that defining features with a good visual interpretation, is indeed important in a scenario, where such an approach is used in an interactive CAD system for pathologists. Considering that problem consists of few classes, and our rather simplistic feature definitions, frameworks can be structured as hierarchies of various binary classifiers. These variants include frameworks which are earlier explored and some which are not explored for this task. We perform various experiments which include traditional texture features and demonstrate the effectiveness of class-specific features in various frameworks. We make insightful comparisons between different types of classification frameworks given their silent aspects and pros and cons over each other. We also demonstrate an experiment with only intermediates samples for testing. The proposed work yields encouraging results with respect to the state-of-the-art and highlights the role of class-specific features in different classification frameworks.
We note however, that the datasets with ICPR-2012, SNP HEp-2 were of a much smaller size (1455, 1884 images), and the larger dataset viz. one with ICPR-2014 containing 13659 images, is generally used by the state-of-the-art methods. We too use the same in this work. Table provides the details of datasets available for HEp-2 cell problem. More recent reviews of this topic were provided by @cite_43 @cite_49 . The reader can refer to @cite_12 for an overview of the literature and of the open challenges in this research area. Below, we discuss some specific works in terms of features and classifiers.
@cite_43 @cite_12 @cite_49
2496191473 2115969689 2095272373 2532520288
Updates the state of the art in HEp-2 cell and specimen image classification.Analyses and compares 15 methods tested over a common and very large dataset.Highlights the key elements of the best performing methods. The Indirect Immunofluorescence (IIF) protocol applied on Human Epithelial type 2 (HEp-2) cells is the current gold standard for the Antinuclear Antibody (ANA) test. The formulation of the diagnosis requires the visual analysis of a patients specimen under a fluorescence microscope in order to recognize the cells staining pattern which could be related to a connective tissue disease. This analysis is time consuming and error prone, thus in the recent past we have witnessed a growing interest in the pattern recognition scientific community directed at the development of methods for supporting this complex task. The main driver of the interest towards this problem is represented by the series of international benchmarking initiatives organized in the last four years that allowed dozens of research groups to propose innovative methodologies for HEp-2 cells staining pattern classification. In this paper we update the state of the art on HEp-2 cells and specimens classification, by analyzing the performance achieved by the methods participating the contest on Performance Evaluation of IIF Image Analysis Systems, hosted by the 22nd edition of the International Conference on Pattern Recognition ICPR 2014, and to the Executable Thematic Special Issue of Pattern Recognition Letters on Pattern Recognition Techniques for IIF Images Analysis, and by highlighting the trends in the design of the best performing methods. Display Omitted Survival prediction of HCC patients is modeled in a real and high complex scenario.Its complexity is due to a small dataset with heterogeneous and missing input data.We propose a new cluster-based oversampling method to overcome these drawbacks.Neural networks and logistic regression are applied in the classification stage.Experimental results show that our method outperforms previous existing approaches. Liver cancer is the sixth most frequently diagnosed cancer and, particularly, Hepatocellular Carcinoma (HCC) represents more than 90 of primary liver cancers. Clinicians assess each patients treatment on the basis of evidence-based medicine, which may not always apply to a specific patient, given the biological variability among individuals. Over the years, and for the particular case of Hepatocellular Carcinoma, some research studies have been developing strategies for assisting clinicians in decision making, using computational methods (e.g. machine learning techniques) to extract knowledge from the clinical data. However, these studies have some limitations that have not yet been addressed: some do not focus entirely on Hepatocellular Carcinoma patients, others have strict application boundaries, and none considers the heterogeneity between patients nor the presence of missing data, a common drawback in healthcare contexts. In this work, a real complex Hepatocellular Carcinoma database composed of heterogeneous clinical features is studied. We propose a new cluster-based oversampling approach robust to small and imbalanced datasets, which accounts for the heterogeneity of patients with Hepatocellular Carcinoma. The preprocessing procedures of this work are based on data imputation considering appropriate distance metrics for both heterogeneous and missing data (HEOM) and clustering studies to assess the underlying patient groups in the studied dataset (K-means). The final approach is applied in order to diminish the impact of underlying patient profiles with reduced sizes on survival prediction. It is based on K-means clustering and the SMOTE algorithm to build a representative dataset and use it as training example for different machine learning procedures (logistic regression and neural networks). The results are evaluated in terms of survival prediction and compared across baseline approaches that do not consider clustering and or oversampling using the Friedman rank test. Our proposed methodology coupled with neural networks outperformed all others, suggesting an improvement over the classical approaches currently used in Hepatocellular Carcinoma prediction models. David Craig and colleagues recently reported methods allowing detection of individual genotypes from summary data of high-density SNP arrays. Eran Halperin and colleagues now report analyses of the statistical power of these methods, employing likelihood ratio statistics to provide an upper-bound to the limits of detection. The continuous decrease in cost of molecular profiling tests is revolutionizing medical research and practice, but it also raises new privacy concerns. One of the first attacks against privacy of biological data, proposed by in 2008, showed that, by knowing parts of the genome of a given individual and summary statistics of a genome-based study, it is possible to detect if this individual participated in the study. Since then, a lot of work has been carried out to further study the theoretical limits and to counter the genome-based membership inference attack. However, genomic data are by no means the only or the most influential biological data threatening personal privacy. For instance, whereas the genome informs us about the risk of developing some diseases in the future, epigenetic biomarkers, such as microRNAs, are directly and deterministically affected by our health condition including most common severe diseases. In this paper, we show that the membership inference attack also threatens the privacy of individuals contributing their microRNA expressions to scientific studies. Our results on real and public microRNA expression data demonstrate that disease-specific datasets are especially prone to membership detection, offering a true-positive rate of up to 77 at a false-negative rate of less than 1 . We present two attacks: one relying on the L_1 distance and the other based on the likelihood-ratio test. We show that the likelihood-ratio test provides the highest adversarial success and we derive a theoretical limit on this success. In order to mitigate the membership inference, we propose and evaluate both a differentially private mechanism and a hiding mechanism. We also consider two types of adversarial prior knowledge for the differentially private mechanism and show that, for relatively large datasets, this mechanism can protect the privacy of participants in miRNA-based studies against strong adversaries without degrading the data utility too much. Based on our findings and given the current number of miRNAs, we recommend to only release summary statistics of datasets containing at least a couple of hundred individuals.