Search

This paper summarizes the discussions held during the First IEEE Life Sciences Grand Challenges Conference, held on October 4–5, 2012, at the National Academy of Sciences, Washington, DC, and the grand challenges identified by the conference participants. Despite tremendous efforts to develop the knowledge and ability that are essential in addressing biomedical and health problems using engineering methodologies, the optimization of this approach toward engineering the life sciences and healthcare remains a grand challenge. The conference was aimed at high-level discussions by participants representing various sectors, including academia, government, and industry. Grand challenges were identified by the conference participants in five areas including engineering the brain and nervous system; engineering the cardiovascular system; engineering of cancer diagnostics, therapeutics, and prevention; translation of discoveries to clinical applications; and education and training. A number of these challenges are identified and summarized in this paper. [ABSTRACT FROM PUBLISHER]

We present a study of image features for cancer diagnosis and Gleason grading of the histological images of prostate. In diagnosis, the tissue image is classified into the tumor and nontumor classes. In Gleason grading, which characterizes tumor aggressiveness, the image is classified as containing a low- or high-grade tumor. The image sets used in this paper consisted of 367 and 268 color images for the diagnosis and Gleason grading problems, respectively, and were captured from representative areas of hematoxylin and eosin-stained tissue retrieved from tissue microarray cores or whole sections. The primary contribution of this paper is to aggregate color, texture, and morphometric cues at the global and histological object levels for classification. Features representing different visual cues were combined in a supervised learning framework. We compared the performance of Gaussian, k-nearest neighbor, and support vector machine classifiers together with the sequential forward feature selection algorithm. On diagnosis, using a five-fold cross-validation estimate, an accuracy of 96.7% was obtained. On Gleason grading, the achieved accuracy of classification into low- and high-grade classes was 81.0%. [ABSTRACT FROM AUTHOR]