James Monaco

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The identification of phenotypic changes in breast cancer (BC) histopathology on account of corresponding molecular changes is of significant clinical importance in predicting disease outcome. One such example is the presence of lymphocytic infiltration (LI) in histopathology, which has been correlated with nodal metastasis and distant recurrence in HER2+(More)
In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80Kx70K pixels - far too many for current automated Gleason(More)
The demand for personalized health care requires a wide range of diagnostic tools for determining patient prognosis and theragnosis (response to treatment). These tools present us with data that is both multi-modal (imaging and non-imaging) and multi-scale (pro-teomics, histology). By utilizing the information in these sources concurrently, we expect(More)
Functional (i.e., logic) verification of the current generation of complex, super-scalar microprocessors such as the PowerPC 604™ microprocessor presents significant challenges to a project's verification participants. Simple architectural level tests are insufficient to gain confidence in the quality of the design. Detailed planning must be combined with a(More)
BACKGROUND Supervised classifiers for digital pathology can improve the ability of physicians to detect and diagnose diseases such as cancer. Generating training data for classifiers is problematic, since only domain experts (e.g. pathologists) can correctly label ground truth data. Additionally, digital pathology datasets suffer from the "minority class(More)
— Annually in the US 186, 000 men are diagnosed with prostate cancer (CaP) and over 43, 000 die from it. The analysis of whole-mount histological sections (WMHSs) is needed to help determine treatment following prostatectomy and to create the " ground truths " of CaP spatial extent required to evaluate other diagnostic modalities (eg. magnetic resonance(More)
Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial extent of prostate cancer (CaP); the most common source for these labels is expert segmentations. When ancillary data such as whole mount(More)
INTRODUCTION The advent of digital slides offers new opportunities within the practice of pathology such as the use of image analysis techniques to facilitate computer aided diagnosis (CAD) solutions. Use of CAD holds promise to enable new levels of decision support and allow for additional layers of quality assurance and consistency in rendered diagnoses.(More)
For personalization of medicine, increasingly clinical and demographic data are integrated into nomograms for prognostic use, while molecular biomarkers are being developed to add independent diagnostic, prognostic, or management information. In a number of cases in surgical pathology, morphometric quantitation is already performed manually or(More)