In this paper we investigate the use of the area under the receiver operating characteristic (ROC)
curve (AUC) as a performance measure for machine learning algorithms. As a case …

We present a review of perceptual image quality metrics and their application to still image
compression. The review describes how image quality metrics can be used to guide an …

Diabetes mellitus is a chronic disease and a major public health challenge worldwide. According
to the International Diabetes Federation, there are currently 246 million diabetic people …

We present an integrated methodology for detecting, segmenting and classifying breast
masses from mammograms with minimal user intervention. This is a long standing problem due …

We show two important findings on the use of deep convolutional neural networks (CNN) in
medical image analysis. First, we show that CNN models that are pre-trained using computer …

International challenges have become the standard for validation of biomedical image
analysis methods. Given their scientific impact, it is surprising that a critical analysis of common …

Mass detection from mammograms plays a crucial role as a pre- processing stage for mass
segmentation and classification. The detection of masses from mammograms is considered …

Over the last decade, support vector machine classifiers (SVMs) have demonstrated
superior generalization performance to many other classification techniques in a variety of …

In this paper, we present an improved algorithm for the segmentation of cytoplasm and nuclei
from clumps of overlapping cervical cells. This problem is notoriously difficult because of …

In this paper, we explore the use of deep convolution and deep belief networks as potential
functions in structured prediction models for the segmentation of breast masses from …