An Introduction to Adversarially Robust Deep Learning.

The widespread success of deep learning in solving machine learning problems has fueled its adoption in many fields, from speech recognition to drug discovery and medical imaging. However, deep learning systems are extremely fragile: imperceptibly small modifications to their input data can cause the models to produce erroneous output. It is very easy to generate such adversarial perturbations even for state-of-the-art models, yet immunization against them has proven exceptionally challenging.

An Unsupervised Transfer Learning Framework for Visible-Thermal Pedestrian Detection.

Dual cameras with visible-thermal multispectral pairs provide both visual and thermal appearance, thereby enabling detecting pedestrians around the clock in various conditions and applications, including autonomous driving and intelligent transportation systems. However, due to the greatly varying real-world scenarios, the performance of a detector trained on a source dataset might change dramatically when evaluated on another dataset. A large amount of training data is often necessary to guarantee the detection performance in a new scenario.

Shared decision-making in advance care planning for persons with dementia in nursing homes: a cross-sectional study.

Background: Shared decision-making provides an approach to discuss advance care planning in a participative and informed manner, embodying the principles of person-centered care. A number of guided approaches to achieve shared decision-making already exist, such as the three-talk model. However, it is uncertain whether daily practice methods in nursing home wards for persons with dementia comply with the underpinnings of this model.

An interactive ImageJ plugin for semi-automated image denoising in electron microscopy.

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data.

Improving shared decision-making in advance care planning: Implementation of a cluster randomized staff intervention in dementia care.

Objective: Increasing staff engagement level of shared decision-making in advance care planning for persons with dementia in nursing homes. Perceived importance, competence and frequency of staff members applying shared decision-making were measured. Additionally, facilitators and barriers in the implementation process were described.

'We DECide optimized' - training nursing home staff in shared decision-making skills for advance care planning conversations in dementia care: protocol of a pretest-posttest cluster randomized trial.

Background: Due to the gradual loss of function, it is crucial for persons with dementia to discuss advance care planning in due course. However, nursing home staff, residents and their families feel uncomfortable to start this type of conversation, resulting in unknown (care) preferences. 'We DECide optimized' will provide tools to nursing home staff for discussing advance care planning.

Line-Constrained Camera Location Estimation in Multi-Image Stereomatching.

Stereomatching is an effective way of acquiring dense depth information from a scene when active measurements are not possible. So-called lightfield methods take a snapshot from many camera locations along a defined trajectory (usually uniformly linear or on a regular grid-we will assume a linear trajectory) and use this information to compute accurate depth estimates. However, they require the locations for each of the snapshots to be known: the disparity of an object between images is related to both the distance of the camera to the object and the distance between the camera positions for both images.

Coxiella burnetii (Q fever) prevalence in associated populations of humans and small ruminants in The Gambia.

Objectives: To simultaneously estimate the prevalence of antibodies against Coxiella burnetii (Q fever) among adults and small ruminants, and C. burnetii shedding prevalence among small ruminants in households in the Kiang West district of The Gambia, and to assess associated risk factors.

Methods: Sera of 599 adults and 615 small ruminants from 125 compounds within 12 villages were tested for antibodies against C.

MATE: Machine Learning for Adaptive Calibration Template Detection.

The problem of camera calibration is two-fold. On the one hand, the parameters are estimated from known correspondences between the captured image and the real world. On the other, these correspondences themselves-typically in the form of chessboard corners-need to be found.

Low Seroprevalence of Brucellosis in Humans and Small Ruminants in the Gambia.

Background: Brucellosis is a worldwide zoonosis with significant impact on rural livelihoods and a potentially underestimated contributor to febrile illnesses. The aim of this study was to estimate the seroprevalence of brucellosis in humans and small ruminants in The Gambia.

Methods: The study was carried out in rural and urban areas.

Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves.

Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results.

Nonparametric estimation receiver operating characteristic analysis for performance evaluation on combined detection and estimation tasks.

In an effort to generalize task-based assessment beyond traditional signal detection, there is a growing interest in performance evaluation for combined detection and estimation tasks, in which signal parameters, such as size, orientation, and contrast are unknown and must be estimated. One motivation for studying such tasks is their rich complexity, which offers potential advantages for imaging system optimization. To evaluate observer performance on combined detection and estimation tasks, Clarkson introduced the estimation receiver operating characteristic (EROC) curve and the area under the EROC curve as a summary figure of merit.

MRI segmentation of the human brain: challenges, methods, and applications.

Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature.

Automatic high-bandwidth calibration and reconstruction of arbitrarily sampled parallel MRI.

Today, many MRI reconstruction techniques exist for undersampled MRI data. Regularization-based techniques inspired by compressed sensing allow for the reconstruction of undersampled data that would lead to an ill-posed reconstruction problem. Parallel imaging enables the reconstruction of MRI images from undersampled multi-coil data that leads to a well-posed reconstruction problem.

Coxiella burnetii seroprevalence in small ruminants in The Gambia.

Background: Q fever is a zoonosis caused by Coxiella burnetii, a Gram negative bacterium present worldwide. Small ruminants are considered the main reservoirs for infection of humans. This study aimed to estimate the extent of C.

Channelized model observer for the detection and estimation of signals with unknown amplitude, orientation, and size.

As a task-based approach for medical image quality assessment, model observers (MOs) have been proposed as surrogates for human observers. While most MOs treat only signal-known-exactly tasks, there are few studies on signal-known-statistically (SKS) MOs, which are clinically more relevant. In this paper, we present a new SKS MO named channelized joint detection and estimation observer (CJO), capable of detecting and estimating signals with unknown amplitude, orientation, and size.

Computationally efficient locally adaptive demosaicing of color filter array images using the dual-tree complex wavelet packet transform.

Most digital cameras use an array of alternating color filters to capture the varied colors in a scene with a single sensor chip. Reconstruction of a full color image from such a color mosaic is what constitutes demosaicing. In this paper, a technique is proposed that performs this demosaicing in a way that incurs a very low computational cost.

Short communication: prevalence of antibodies against Coxiella burnetii (Q fever) in children in The Gambia, West Africa.

Objective: To estimate the prevalence of antibodies against Coxiella burnetii (Q fever) among children in eight villages in The Gambia, West Africa.

Methods: Sera of 796 children aged 1-15 years were tested for presence of antibodies against phase II of C. burnetii by ELISA.

Channelized Hotelling observers for the assessment of volumetric imaging data sets.

Current clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For three-dimensional (3D) images, however, these models have been little explored so far.

Image denoising using mixtures of projected Gaussian Scale Mixtures.

We propose a new statistical model for image restoration in which neighborhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian Scale Mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighborhood is obtained, thereby modeling the strongest correlations in that neighborhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techniques.

Removal of correlated noise by modeling the signal of interest in the wavelet domain.

Images, captured with digital imaging devices, often contain noise. In literature, many algorithms exist for the removal of white uncorrelated noise, but they usually fail when applied to images with correlated noise. In this paper, we design a new denoising method for the removal of correlated noise, by modeling the significance of the noise-free wavelet coefficients in a local window using a new significance measure that defines the "signal of interest" and that is applicable to correlated noise.

Trypanotolerance in small ruminants of sub-Saharan Africa.

Although a lot of information is currently available on trypanotolerance in cattle, until recently the trypanotolerant nature of small ruminants was not well known. Trypanotolerance in small ruminants is less pronounced than in cattle and should be considered as resilience rather than resistance. West African Dwarf (WAD) goats seem to be less trypanotolerant than Djallonke sheep.