Gender Determination using Fingerprint Features

Several previous studies have investigated the gender difference of the fingerprint features. However, regarding to the statistical significance of such differences, inconsistent results have been obtained. To resolve this problem and to develop a method for gender determination, this work proposes and tests three fingertip features for gender determination. Fingerprints were obtained from 115 normal healthy adults comprised of 57 male and 58 female volunteers. All persons were born in Taiwan and were of Han nationality. The age range was18-35 years. The features of this study are ridge count, ridge density, and finger size, all three of which can easily be determined by counting and calculation. Experimental results show that the tested ridge density features alone are not very effective for gender determination. However, the proposed ridge count and finger size features of left little fingers are useful, achieving a classification accuracy of 75% (P-valu

A Comparative Study for 2D and 3D Computer-aided Diagnosis Methods for Solitary Pulmonary Nodules

Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to investigate the relative diagnostic accuracy of 2D and 3D methods. An additional goal is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand

SASMU: boost the performance of generalized recognition model using synthetic face dataset

Nowadays, deploying a robust face recognition product becomes easy with the development of face recognition techniques for decades. Not only profile image verification but also the state-of-the-art method can handle the in-the-wild image almost perfectly. However, the concern of privacy issues raise rapidly since mainstream research results are powered by tons of web-crawled data, which faces the privacy invasion issue. The community tries to escape this predicament completely by training the face recognition model with synthetic data but faces severe domain gap issues, which still need to access real images and identity labels to fine-tune the model. In this paper, we propose SASMU, a simple, novel, and effective method for face recognition using a synthetic dataset. Our proposed method consists of spatial data augmentation (SA) and spectrum mixup (SMU). We first analyze the existing synthetic datasets for developing a face recognition system. Then, we reveal that heavy data augmentation is helpful for boosting performance when using synthetic data. By analyzing the previous frequency mixup studies, we proposed a novel method for domain generalization. Extensive experimental results have demonstrated the effectiveness of SASMU, achieving state-of-the-art performance on several common benchmarks, such as LFW, AgeDB-30, CA-LFW, CFP-FP, and CP-LFW.Comment: under revie

A Two‐Stage Comittee Machine of Neural Networks

In solving pattern recognition problems, many ensemble methods have been proposed to replace a single classifier by a classifier committee. These methods can be divided roughly into two categories: serial and parallel approaches. In the serial approach, component classifiers are created by focusing on different parts of the training set in different learning phases. In contrast, without paying special attention to any part of the dataset, the parallel approach generates classifiers independently. By integrating these two techniques and by using a neural network approach for the base classifier, this work proposes a design method for a two‐stage committee machine. In the first stage of the approach the entire dataset is used to train an averaging ensemble. Based on the classification results of this first stage, hard‐to‐classify samples are selected and sent to the second stage. To improve the classification accuracy for these samples, a computationally more intensive bagging ensemble is employed in the second stage. These two neural network ensembles work in series whereas the component neural networks in each of the ensembles are trained in parallel. Experimental results demonstrate the accuracy and robustness of the proposed approach

CT attenuation features of individual calcified coronary plaque: differences among asymptomatic, stable angina pectoris, and acute coronary syndrome groups.

Coronary artery calcium (CAC) assessed by non-contrast cardiac CT has been shown to be an independent factor from the Framingham risk factors in predicting cardiovascular events. However, many patients with acute coronary syndrome (ACS) have low CAC score. A recent study that re-analyzed the previous CAC CT scan of MESA cohort showed that in subjects with global lower density, CAC was associated with higher risk of ACS. We aimed to further evaluate the characteristics of CAC attenuation features in ACS subjects, in comparison to asymptomatic and stable angina pectoris (SAP) groups.In a period of 18 months, 524 consecutive subjects received standard CAC CT scans in our department; 278 of 524 subjects with presence of CAC (225 men, age = 60.6±9.5 years; ACS = 41, SAP = 78, asymptomatic = 159) were enrolled. Agatston score, number of plaques (NP) per subject and mean (HMEAN) and standard deviation (HSD) of attenuation of each calcified plaque were measured. Three regression models to distinguish the groups were built: model 1, conventional risk factors only; model 2, Agatston score plus model 1; model 3, plaque attenuation features plus model 2.Agatston score in ACS group (median = 112.9) was higher than in the asymptomatic group (median = 54.4, P = 0.028) and similar to the SAP group (median = 237.8, P = 0.428). Calcified plaques in the ACS group showed lower (HMEAN = 180.5) and more homogenous (HSD = 31.2) attenuation than those of the asymptomatic group (HMEAN = 205.9, P = 0.002; HSD = 52.4, P = 0.006) and the SAP group (HMEAN = 204.1, P = 0.016; HSD = 54.4, P = 0.011). Model 3 significantly improved the distinction between ACS and asymptomatic groups (area under curve [AUC] = 0.93) as compared to model 2 (AUC = 0.83, P = 0.003) and model 1 (AUC = 0.79, P = 0.001).Calcified plaques in the ACS group were characteristically of low and homogenous CT attenuation. With validation in a large cohort, analysis of CT attenuation features may improve risk stratification of ACS using CAC CT scan

Clinical Characteristics and Distribution of Agatston Scores of the Three Subject Groups.

<p>*by Chi-square test or ANOVA</p><p>ACS: acute coronary syndrome; AS: Agatston score; CAD: coronary artery disease; HDL-C = high-density lipoprotein cholesterol; SAP = stable angina pectoris</p><p>Clinical Characteristics and Distribution of Agatston Scores of the Three Subject Groups.</p