BACKGROUND: Determine the effects of race, socioeconomic status, and treatment on outcomes for patients diagnosed with lung cancer. METHODS: The Florida cancer registry and inpatient and ambulatory data were queried for patients diagnosed from 1998-2002. RESULTS: A total 76,086 of lung cancer patients were identified. Overall, 55.6% were male and 44.4% were female. The demographic distribution of patients was 92.7% Caucasian, 6.7% African American, and 5.7% Hispanic. The mean age of diagnosis was 70 years old. African American patients presented at a younger age, with more advanced disease, and were less likely to undergo surgical therapy than their Caucasian counterparts. Median survival time (MST) for the entire cohort was 8.7 months, while MST for African American patients was 7.5 months. Patients who received surgery, chemotherapy, or radiation therapy demonstrated significantly improved outcomes. Stepwise multivariate analysis revealed that African American race was no longer a statistically significant predictor of worse outcomes once corrections were made for demographics and comorbid conditions, suggesting that the originally reported disparities in lung cancer outcomes and race may be in part because of poor pretreatment performance status. In contrast, patients of the lowest socioeconomic status continue to have a slightly worse overall prognosis than their affluent counterparts (hazard ratio ¼ 1.05, P ¼ .001). CONCLU-SIONS: Lung cancer continues to carry a poor prognosis for all patients. Once comorbidities are corrected for, African American patients carry equivalently poor outcomes. Nonetheless, emphasis must be placed on improving pretreatment performance status among African American patients and efforts for earlier diagnosis among the impoverished patients must be made.

This article describes how to use the IEEEtran class with L A T E X to produce high quality typeset papers that are suitable for submission to the Institute of Electrical and Electronics Engineers (IEEE). IEEEtran can produce conference, journal and technical note (correspondence) papers with a suitable choice of class options. This document was produced using IEEEtran in journal mode.

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNet

This paper proposes a new technique for face detection and lip feature extraction. A real-time field-programmable gate array (FPGA) implementation of the two proposed techniques is also presented. Face detection is based on a naive Bayes classifier that classifies an edge-extracted representation of an image. Using edge representation significantly reduces the model's size to only 5184 B, which is 2417 times smaller than a comparable statistical modeling technique, while achieving an 86.6% correct detection rate under various lighting conditions. Lip feature extraction uses the contrast around the lip contour to extract the height and width of the mouth, metrics that are useful for speech filtering. The proposed FPGA system occupies only 15050 logic cells, or about six times less than a current comparable FPGA face detection system.