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Local dermoscopic features are useful in many dermoscopic criteria for skin cancer detection. We address the problem of detecting local dermoscopic features from epiluminescence (ELM) microscopy skin lesion images. We formulate the recognition of local dermoscopic features as a multi-instance learning (MIL) problem. We employ the method of diverse density (DD) and evidence confidence (EC) function...
The combined use of gene expression profiles and protein-protein interaction (PPI) networks has recently shed light on breast cancer research by selecting a small number of subnetworks as disease markers and then using them for the classification of metastasis. Based on previously identified subnetwork markers, we compare three ensemble learning approaches (AdaBoost, LogitBoost and random forest)...
Digital mammography is one of the most promising options to diagnose breast cancer which is the most common cancer in women. However, it has weaknesses in excessive unnecessary biopsy referrals that enfeeble its effectiveness due to difficulty in distinguishing actual cancer lesions from benign abnormalities. To overcome this issue, computer aided diagnosis (CADx) using machine learning techniques...
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