An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images (SWI). The proposed framework consists of four main phases (i) making clusters of brain Magnetic Resonance Imaging (MRI) using k-mean classifier (ii) reduce false positives for better classification results (iii) discriminative feature extraction specific to CMBs (iv) classification using a five layers convolutional neural network (CNN). The proposed method is evaluated on a public dataset available for 20 subjects. The proposed system shows an accuracy of 98.9% and a 1.1% false-positive rate value. The results show the superiority of the proposed work as compared to existing states of the art methods.     

Recognition memory for novel stimuli: The structural regularity hypothesis.

Early studies of human memory suggest that adherence to a known structural regularity (e.g., orthographic regularity) benefits memory for an otherwise novel stimulus (e.g., G. A. Miller, 1958). However, a more recent study suggests that structural regularity can lead to an increase in false-positive responses on recognition memory tests (B. W. A. Whittlesea & L. D. Williams, 1998). In the present study the authors attempted to identify the circumstances under which structural regularity benefits old-new discrimination and those under which it leads to an increase in false-positive responses. The highly generalizable tendency shown here is for structural regularity to benefit old-new discrimination. The increase in false-positive responses for structurally regular novel items may be limited to situations in which regularity is confounded with similarity to studied items. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

微生物法检测乳中抗生素残留及假阳性产生的原因

介绍了乳中抗生素残留检测的常用微生物法氯化三苯四氮唑法、纸片法、活性试验法、试剂盒方法,并从乳本身因素、外来因素2个角度分析实际检测过程中引起假阳性结果的原因,为乳中抗生素检测提供理论参考。     

Spatio-temporal context based recurrent visual attention model for lymph node detection

False-positive reduction is one of the most crucial components in an automated lymph nodes (LNs) detection task in volumetric computed tomography (CT) scans, which is a highly sought goal for cancer diagnosis and early treatment. In this article, treating the three-dimensional (3D) LN detection task as object detection on sequence problem, we propose a novel spatio-temporal context-based recurrent visual attention model (STRAM) for the LNs false positive reduction. We firstly extract the deep spatial features maps for two-dimensional LN patches from pre-trained Inception-V3 model. A new Gaussian kernel-based spatial attention method is then presented to extract the most discriminating spatial features for the corresponding center slices. Additionally, to combine the temporal information between 3D CT slices, we devise a novel “Siamese” mixture density networks which can learn to adaptively focus on the most relevant parts of the CT slices. Considering the lesion areas always locate around the centroid of the 3D CT scans, a hard constraint is imposed on the predicted attention locations with batch normalization technique and the Siamese architecture. The proposed model is a fully differentiable unit that can be optimized end-to-end by using stochastic gradient descent. The effectiveness of our method is verified on LN dataset: 388 mediastinal LNs labeled by radiologists in 90 patient CT scans, and 595 abdominal LNs in 86 patient CT scans. Our method demonstrates sensitivities of about 87%/82% at 3 FP/vol. and 93%/89% at 6 FP/vol. for mediastinum and abdomen, respectively, which compares favorably to previous methods.     

Comparison of multiple methods for the determination of sulphite in Allium and Brassica vegetables

Sulphites are a family of additives regulated for use worldwide in food products. They must be declared on the label if they are present in concentrations greater than 10 mg kg^–1, determined as sulphur dioxide (SO₂). The current US regulatory method for sulphites, the optimised Monier–Williams method (OMW), produces false-positive results with vegetables from the Allium (garlic) and Brassica (cabbage) genera due to extraction conditions that are thought to cause endogenous sulphur compounds to release SO₂. Recently, modifications to the OMW method (2x MW) were published that reportedly reduced this false-positive in garlic. However, no other vegetables from these genera have been investigated. In addition, an LC-MS/MS method was developed for sulphite analysis, but it has not yet been tested with these problematic matrices. Ten vegetable species were analysed using these sulphite methods (OMW titration, OMW gravimetric, 2x MW and LC-MS/MS) to determine the false-positive rate. Sulphite concentrations > 10 mg kg^–1 SO₂ were observed with the OMW analyses. The 2x MW method reduced the measured concentration in unsulphited samples to ≤ 10 mg kg^–1 SO₂ for all matrices analysed. The LC-MS/MS method showed concentrations < 10 mg kg^–1 for the Brassica samples, but only displayed a slight reduction in the Allium matrices. Spiked recovery studies were conducted to determine if these methods can detect added sulphite. The 2x MW had recoveries of 17% and 42% for water and fresh garlic, respectively, and the LC-MS/MS had recoveries of 108%, 125%, 116% and 107% for water, fresh garlic, roasted garlic, and hummus, respectively. The low recoveries of the 2x MW may indicate that sulphur compounds cannot be properly quantified with this method. The ability to eliminate false-positives will enable accurate determination of added sulphite to ensure compliance with sulphite labelling requirements.