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The ability to accurately interpret large image scenes is often dependent on the ability to extract relevant contextual, domain-specific information from different parts of the scene. Traditionally, techniques such as multi-scale (i.e. multi-resolution) frameworks and hierarchical classifiers have been used to analyze large images. In this paper we present a novel framework that classifies entire...
Gleason grading of prostate cancer is complicated by cancer confounders, or benign tissues closely resembling malignant processes (e.g. atrophy), which account for as much as 34% of misdiagnoses. Thus, it is critical to correctly identify confounders in a computer-aided diagnosis system. In this work, we present a cascaded multi-class pairwise classifier (CascaMPa) to identify the class of regions...
Triple-negative (TN) breast cancer has gained much interest recently due to its lack of response to receptor-targeted therapies and its aggressive clinical nature. In this study, we evaluate the ability of a computer-aided diagnosis (CAD) system to not only distinguish benign from malignant lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but also to quantitatively distinguish...
In this paper we present a new robust method for medical image registration called combined feature ensemble mutual information (COFEMI). While mutual information (MI) has become arguably the most popular similarity metric for image registration, intensity based MI schemes have been found wanting in inter-modal and interprotocol image registration, especially when (1) significant image differences...
In this paper we present a computer-aided diagnosis (CAD) system to automatically detect prostatic adenocarcinoma from high resolution digital histopathological slides. This is especially desirable considering the large number of tissue slides that are currently analyzed manually - a laborious and time-consuming task. Our methodology is novel in that texture-based classification is performed using...
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