Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease

Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D CNN to detect Alzheimer's disease based on structural MRI scans of the brain. Then, we apply four different gradient-based and occlusion-based visualization methods that explain the network's classification decisions by highlighting relevant areas in the input image. We compare the methods qualitatively and quantitatively. We find that all four methods focus on brain regions known to be involved in Alzheimer's disease, such as inferior and middle temporal gyrus. While the occlusion-based methods focus more on specific regions, the gradient-based methods pick up distributed relevance patterns. Additionally, we find that the distribution of relevance varies across patients, with some having a stronger focus on the temporal lobe, whereas for others more cortical areas are relevant. In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.Comment: MLCN 201

Integral open building design methodology

There is a growing awareness of sustainability that leads towards knowledge transfer and research between companies and the Dutch knowledge and research institutes within the building industry. The principles of the IFD (Industrial Flexible Dismountable) concept aim at an integrated approach within the design process to reach a maximum level of integration between designers from different disciplines. A newly developed methodology for structuring integral design processes enables design team support during designing and further stimulates exchange of ideas and concepts. This approach is tested within a professional context of a building design project. To support architects more effectively with their tasks the domain-independent integral design methodology was developed in the lines with Open building This specific multi-disciplinary approach helpd architecture and engineering. We think that the proposed Integral Design methodology is a possible solution for support of the design team in the conceptual phase of building design

VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

Robust face detection in the wild is one of the ultimate components to support various facial related problems, i.e. unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenarios due to numerous challenges, e.g. heavy facial occlusions, extremely low resolutions, strong illumination, exceptionally pose variations, image or video compression artifacts, etc. In this paper, we present a face detection approach named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN) to robustly solve the problems mentioned above. Similar to the region-based CNNs, our proposed network consists of the region proposal component and the region-of-interest (RoI) detection component. However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. Firstly, the multi-scale information is grouped both in region proposal and RoI detection to deal with tiny face regions. Secondly, our proposed network allows explicit body contextual reasoning in the network inspired from the intuition of human vision system. The proposed approach is benchmarked on two recent challenging face detection databases, i.e. the WIDER FACE Dataset which contains high degree of variability, as well as the Face Detection Dataset and Benchmark (FDDB). The experimental results show that our proposed approach trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE Dataset by a large margin, and consistently achieves competitive results on FDDB against the recent state-of-the-art face detection methods

Analysis of the complex rheological properties of highly concentrated proteins with a closed cavity rheometer

Highly concentrated biopolymers are used in food extrusion processing. It is well known that rheological properties of biopolymers influence considerably both process conditions and product properties. Therefore, characterization of rheological properties under extrusionrelevant conditions is crucial to process and product design. Since conventional rheological methods are still lacking for this purpose, a novel approach is presented. A closed cavity rheometer known in the rubber industry was used to systematically characterize a highly concentrated soy protein, a very relevant protein in extruded meat analogues. Rheological properties were first determined and discussed in the linear viscoelastic range (SAOS). Rheological analysis was then carried out in the non-linear viscoelastic range (LAOS), as high deformations in extrusion demand for measurements at process-relevant high strains. The protein showed gel behavior in the linear range, while liquid behavior was observed in the nonlinear range. An expected increase in elasticity through addition of methylcellulosewas detected. The measurements in the non-linear range reveal significant changes of material behavior with increasing strain. As another tool for rheological characterization, a stress relaxation test was carried out which confirmed the increase of elastic behavior after methylcellulose addition

Current state of high-fidelity multimodal monitoring in traumatic brain injury

Introduction Multimodality monitoring of patients with severe traumatic brain injury (TBI) is primarily performed in neurocritical care units to prevent secondary harmful brain insults and facilitate patient recovery. Several metrics are commonly monitored using both invasive and non-invasive techniques. The latest Brain Trauma Foundation guidelines from 2016 provide recommendations and thresholds for some of these. Still, high-level evidence for several metrics and thresholds is lacking. Methods Regarding invasive brain monitoring, intracranial pressure (ICP) forms the cornerstone, and pressures above 22 mmHg should be avoided. From ICP, cerebral perfusion pressure (CPP) (mean arterial pressure (MAP)-ICP) and pressure reactivity index (PRx) (a correlation between slow waves MAP and ICP as a surrogate for cerebrovascular reactivity) may be derived. In terms of regional monitoring, partial brain tissue oxygen pressure (PbtO(2)) is commonly used, and phase 3 studies are currently ongoing to determine its added effect to outcome together with ICP monitoring. Cerebral microdialysis (CMD) is another regional invasive modality to measure substances in the brain extracellular fluid. International consortiums have suggested thresholds and management strategies, in spite of lacking high-level evidence. Although invasive monitoring is generally safe, iatrogenic hemorrhages are reported in about 10% of cases, but these probably do not significantly affect long-term outcome. Non-invasive monitoring is relatively recent in the field of TBI care, and research is usually from single-center retrospective experiences. Near-infrared spectrometry (NIRS) measuring regional tissue saturation has been shown to be associated with outcome. Transcranial doppler (TCD) has several tentative utilities in TBI like measuring ICP and detecting vasospasm. Furthermore, serial sampling of biomarkers of brain injury in the blood can be used to detect secondary brain injury development. Conclusions In multimodal monitoring, the most important aspect is data interpretation, which requires knowledge of each metric's strengths and limitations. Combinations of several modalities might make it possible to discern specific pathologic states suitable for treatment. However, the cost-benefit should be considered as the incremental benefit of adding several metrics has a low level of evidence, thus warranting additional research.Peer reviewe

High Moisture Extrusion of Soy Protein: Investigations on the Formation of Anisotropic Product Structure

The high moisture extrusion of plant proteins is well suited for the production of protein-rich products that imitate meat in their structure and texture. The desired anisotropic product structure of these meat analogues is achieved by extrusion at high moisture content (>40%) and elevated temperatures (>100 °C); a cooling die prevents expansion of the matrix and facilitates the formation of the anisotropic structure. Although there are many studies focusing on this process, the mechanisms behind the structure formation still remain largely unknown. Ongoing discussions are based on two very different hypotheses: structure formation due to alignment and stabilization of proteins at the molecular level vs. structure formation due to morphology development in multiphase systems. The aim of this paper is, therefore, to investigate the mechanism responsible for the formation of anisotropic structures during the high moisture extrusion of plant proteins. A model protein, soy protein isolate, is extruded at high moisture content and the changes in protein–protein interactions and microstructure are investigated. Anisotropic structures are achieved under the given conditions and are influenced by the material temperature (between 124 and 135 °C). Extrusion processing has a negligible effect on protein–protein interactions, suggesting that an alignment of protein molecules is not required for the structure formation. Instead, the extrudates show a distinct multiphase system. This system consists of a water-rich, dispersed phase surrounded by a water-poor, i.e., protein-rich, continuous phase. These findings could be helpful in the future process and product design of novel plant-based meat analogues

Application of Robotic Transcranial Doppler for Extended Duration Recording in Moderate/Severe Traumatic Brain Injury: First Experiences

Long duration application of transcranial Doppler (TCD) for recording of middle cerebral artery (MCA) cerebral blood flow velocity (CBFV) has been fraught with difficulties.[1,2] Classically, TCD has been labor intensive, with limited ability to obtain uninterrupted recordings for extended periods. Furthermore, application of TCD within neurocritically ill for long durations has been limited given the complexity of care, regular bedside nursing care/patient manipulations, and presence of various other multi-modal monitoring devices. This is especially the case in traumatic brain injury (TBI) patients, with the adoption of extensive multi-modal monitoring. Within TBI, most TCD recordings, using standard widely available probes and holders, range from 30 minutes to 1-hour duration and are frequently interrupted due to shifting of the probe and signal loss.[3,4] Thus, we are typically left with a “snap-shot” recording with TCD examination, limiting our ability to extract valuable continuous variables, such as autoregulatory capacity.[3-5] Recent advances in robotics have le

ICP versus Laser Doppler Cerebrovascular Reactivity Indices to Assess Brain Autoregulatory Capacity

Objective: To explore the relationship between various autoregulatory indices in order to determine which approximate small-vessel/microvascular autoregulatory capacity most accurately. Methods: Utilizing a retrospective cohort of traumatic brain injury (TBI) patients (N=41) with: transcranial Doppler (TCD), intracranial pressure (ICP) and cortical laser Doppler flowmetry (LDF), we calculated various continuous indices of autoregulation and cerebrovascular responsiveness: A. ICP derived (pressure reactivity index (PRx) – correlation between ICP and mean arterial pressure (MAP), PAx – correlation between pulse amplitude of ICP (AMP) and MAP, RAC – correlation between AMP and cerebral perfusion pressure (CPP)), B. TCD derived (Mx – correlation between mean flow velocity (FVm) and CPP, Mx_a – correlation betrween FVm and MAP, Sx – correlation between systolic flow velocity (FVs) and CPP, Sx_a – correlation between FVs and MAP, Dx – correlation between diastolic flow index (FVd) and CPP, Dx_a – correlation between FVd and MAP), and LDF derived (Lx – correlation between LDF cerebral blood flow (CBF) and CPP, Lx_a – correlation between LDF-CBF and MAP). We assessed the relationship between these indices via Pearson correlation, Friedman test, principal component analysis (PCA), agglomerative hierarchal clustering (AHC) and k-means cluster analysis (KMCA). Results: LDF based autoregulatory index (Lx) was most associated with TCD based Mx/Mx_a and Dx/Dx_a across Pearson correlation, PCA, AHC and KMCA. Lx was only remotely associated with ICP based indices (PRx, PAx, RAC). TCD based Sx/Sx_a were more closely associated with ICP derived PRx, PAx and RAC. This indicates that vascular derived indices of autoregulatory capacity (ie. TCD and LDF based) co-vary, with Sx/Sx_a being the exception. Whereas, indices of cerebrovascular reactivity derived from pulsatile CBV (ie. ICP indices) appear to not be closely related to those of vascular origin. Conclusions: Transcranial Doppler Mx is the most closely associated with LDF based Lx/Lx_a. Both Sx/Sx-a and the ICP derived indices appear to be dissociated with LDF based cerebrovascular reactivity, leaving Mx/Mx-a as a better surrogate for the assessment of cortical small vessel/microvascular cerebrovascular reactivity. Sx/Sx_a co-cluster/co-vary with ICP derived indices, as seen in our previous work.This work was made possible through salary support through the Cambridge Commonwealth Trust Scholarship, the Royal College of Surgeons of Canada – Harry S. Morton Travelling Fellowship in Surgery, the University of Manitoba Clinician Investigator Program, R. Samuel McLaughlin Research and Education Award, the Manitoba Medical Service Foundation, and the University of Manitoba Faculty of Medicine Dean’s Fellowship Fund. These studies were supported by National Institute for Healthcare Research (NIHR, UK) through the Acute Brain Injury and Repair theme of the Cambridge NIHR Biomedical Research Centre, an NIHR Senior Investigator Award to DKM. Authors were also supported by a European Union Framework Program 7 grant (CENTER-TBI; Grant Agreement No. 602150) MC is supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI17C1790). JD is supported by a Woolf Fisher Scholarship (NZ)

Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: The Benefit of Target Expectation Maximization

© 2018, Springer Nature Switzerland AG. In this paper, we make two contributions to unsupervised domain adaptation (UDA) using the convolutional neural network (CNN). First, our approach transfers knowledge in all the convolutional layers through attention alignment. Most previous methods align high-level representations, e.g., activations of the fully connected (FC) layers. In these methods, however, the convolutional layers which underpin critical low-level domain knowledge cannot be updated directly towards reducing domain discrepancy. Specifically, we assume that the discriminative regions in an image are relatively invariant to image style changes. Based on this assumption, we propose an attention alignment scheme on all the target convolutional layers to uncover the knowledge shared by the source domain. Second, we estimate the posterior label distribution of the unlabeled data for target network training. Previous methods, which iteratively update the pseudo labels by the target network and refine the target network by the updated pseudo labels, are vulnerable to label estimation errors. Instead, our approach uses category distribution to calculate the cross-entropy loss for training, thereby ameliorating the error accumulation of the estimated labels. The two contributions allow our approach to outperform the state-of-the-art methods by +2.6% on the Office-31 dataset