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We propose a fast iterative classification algorithm for Kernel Fisher Discriminant (KFD) using heterogeneous kernel models. In contrast with the standard KFD that requires the user to predefine a kernel function, we incorporate the task of choosing an appropriate kernel into the optimization problem to be solved. The choice of kernel is defined as a linear(More)
The existing methods for offline training of cascade classifiers take a greedy search to optimize individual classifiers in the cascade , leading inefficient overall performance. We propose a new design of the cascaded classifier where all classifiers are optimized for the final objective function. The key contribution of this paper is the AND-OR framework(More)
We propose a novel Bayesian multiple instance learning (MIL) algorithm. This algorithm automatically identifies the relevant feature subset, and utilizes inductive transfer when learning multiple (conceptually related) classifiers. Experimental results indicate that the proposed MIL method is more accurate than previous MIL algorithms and selects a much(More)
Automatic analysis of histopathological images has been widely utilized leveraging computational image-processing methods and modern machine learning techniques. Both computer-aided diagnosis (CAD) and content-based image-retrieval (CBIR) systems have been successfully developed for diagnosis, disease detection, and decision support in this area. Recently,(More)
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In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. The continuum of intraductal breast lesions includes the usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). The current standard of care is to perform percutaneous needle biopsies for diagnosis(More)
Pathology slides are diagnosed based on the his-tological descriptors extracted from regions of interest (ROIs) identified on each slide by the pathologists. A slide usually contains multiple regions of interest and a positive (cancer) diagnosis is confirmed when at least one of the ROIs in the slide is identified as positive. For a negative diagnosis the(More)
We describe a method for sparse feature selection for a class of problems motivated by our work in Computer-Aided Detection (CAD) systems for identifying structures of interest in medical images. Typical CAD data sets for classification are large (several thousand candidates) and unbalanced (significantly fewer than 1% of the candidates are " positive ").(More)
Most methods for classifier design assume that the training samples are drawn independently and identically from an unknown data generating distribution , although this assumption is violated in several real life problems. Relaxing this i.i.d. assumption, we consider algorithms from the statistics literature for the more realistic situation where batches or(More)
Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremendously impacted colorectal cancer diagnosis using 3D medical imaging. It is a prerequisite for all CAD systems to extract the air-distended colon segments from 3D abdomen computed tomography scans. In this paper, we present a two-level statistical approach of(More)