• Carcinoma Type Classification From High-Resolution Breast Microscopy Images Using a Hybrid Ensemble of Deep Convolutional Features and Gradient Boosting Trees Classifiers

    1 month ago - By IEEE/ACM

    Breast cancer is one of the main causes behind cancer deaths in women worldwide. Yet, owing to the complexity of the histopathological images and the arduousness of manual analysis task, the entire diagnosis process becomes time-consuming and the results are often contingent on the pathologist's subjectivity. Thus developing an automated, precise histopathological image classification system is crucial. This paper presents a novel hybrid ensemble framework consisting of multiple fine-tuned convolutional neural network architectures as supervised feature extractors and eXtreme gradient...
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  • AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes

    1 month ago - By IEEE/ACM

    Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases and their sub-types. Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and...
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