Multichannel heart scanner based on high-Tc SQUIDs

A 7-channel magnetometer for magnetocardiography based on high-T c SQUIDs has been realized. This magnetometer is used for test experiments in the development of a multichannel high-Tc SQUID based heart-scanner for clinical applications. The intrinsic noise level of the channels in the 7-channel system is typically 120 fT/¿(Hz) down to 1 Hz. Magnetocardiograms were recorded inside a magnetically shielded room. Introductory experiments were performed on the suppression of noise by combining magnetometers to form planar gradiometers. The noise suppression that can be established appeared to be limited by the imbalance of the gradiometric configuration, which is roughly 2%. This relatively poor balance of the system is caused by inaccuracies in the transfer functions of the individual SQUID magnetometers, and by deviations from the planar geometr

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.Comment: Submitted to IEEE TM

Two-step calibration method for multi-algorithm score-based face recognition systems by minimizing discrimination loss

We propose a new method for combining multi-algorithm score-based face recognition systems, which we call the two-step calibration method. Typically, algorithms for face recognition systems produce dependent scores. The two-step method is based on parametric copulas to handle this dependence. Its goal is to minimize discrimination loss. For synthetic and real databases (NIST-face and Face3D) we will show that our method is accurate and reliable using the cost of log likelihood ratio and the information-theoretical empirical cross-entropy (ECE)

Facial recognition using new LBP representations

In this paper, we propose a facial recognition based on the LBP operator. We divide the face into non-overlapped regions. After that, we classify a training set using each region at a time under different configurations of the LBP operator. Regarding to the best recognition rate, we consider a weight and specific LBP configuration to the regions. To represent the face image, we extract LBP histograms with the specific configuration (radius and neighbors) and concatenate them into feature histogram. We propose a multi-resolution approach, to gather local and global information and improve the recognition rate. To evaluate our proposed approach, we considered the FERET data set, which includes different facial expressions, lighting, and aging of the subjects. In addition, weighted Chi-2 is considered as a dissimilarity measure. The experimental results show a considerable improvement against the original idea

Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN

A morph is a combination of two separate facial images and contains identity information of two different people. When used in an identity document, both people can be authenticated by a biometric Face Recognition (FR) system. Morphs can be generated using either a landmark-based approach or approaches based on deep learning such as Generative Adversarial Networks (GAN). In a recent paper, we introduced a \emph{worst-case} upper bound on how challenging morphing attacks can be for an FR system. The closer morphs are to this upper bound, the bigger the challenge they pose to FR. We introduced an approach with which it was possible to generate morphs that approximate this upper bound for a known FR system (white box), but not for unknown (black box) FR systems. In this paper, we introduce a morph generation method that can approximate worst-case morphs even when the FR system is not known. A key contribution is that we include the goal of generating difficult morphs \emph{during} training. Our method is based on Adversarially Learned Inference (ALI) and uses concepts from Wasserstein GANs trained with Gradient Penalty, which were introduced to stabilise the training of GANs. We include these concepts to achieve similar improvement in training stability and call the resulting method Wasserstein ALI (WALI). We finetune WALI using loss functions designed specifically to improve the ability to manipulate identity information in facial images and show how it can generate morphs that are more challenging for FR systems than landmark- or GAN-based morphs. We also show how our findings can be used to improve MIPGAN, an existing StyleGAN-based morph generator