Domain-adversarial neural networks to address the appearance variability of histopathology images

Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab over time, resulting in significant variability of the tissue appearance that hampers the generalization of automatic image analysis methods. Typically, this is addressed with ad-hoc approaches such as staining normalization that aim to reduce the appearance variability. In this paper, we propose a systematic solution based on domain-adversarial neural networks. We hypothesize that removing the domain information from the model representation leads to better generalization. We tested our hypothesis for the problem of mitosis detection in breast cancer histopathology images and made a comparative analysis with two other approaches. We show that combining color augmentation with domain-adversarial training is a better alternative than standard approaches to improve the generalization of deep learning methods.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi

Inferring a Third Spatial Dimension from 2D Histological Images

Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast. This process results in 2D images of the specimen that has a three-dimensional structure. In this paper, we propose a method to infer how the stains are distributed in the direction perpendicular to the surface of the slide for a given 2D image in order to obtain a 3D representation of the tissue. This inference is achieved by decomposition of the staining concentration maps under constraints that ensure realistic decomposition and reconstruction of the original 2D images. Our study shows that it is possible to generate realistic 3D images making this method a potential tool for data augmentation when training deep learning models.Comment: IEEE International Symposium on Biomedical Imaging (ISBI), 201

Automatic segmentation of MR brain images with a convolutional neural network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol

Empowerment of personal injury victims through the internet: design of a randomized controlled trial

Abstract Background Research has shown that current claims settlement process can have a negative impact on psychological and physical recovery of personal injury (PI) victims. One of the explanations for the negative impact on health is that the claims settlement process is a stressful experience and victims suffer from renewed victimization caused by the claims settlement process. PI victims can experience a lack of information, lack of involvement, lack of 'voice', and poor communication. We present the first study that aims to empower PI victims with respect to the negative impact of the claims settlement process by means of an internet intervention. Methods/design The study is a two armed, randomized controlled trial (RCT), in which 170 PI victims are randomized to either the intervention or control group. The intervention group will get access to a website providing 1) an information module, so participants learn what is happening and what to expect during the claims settlement process, and 2) an e-coach module, so participants learn to cope with problems they experience during the claims settlement process. The control group will get access to a website with hyperlinks to commonly available information only. Participants will be recruited via a PI claims settlement office. Participants are included if they have been involved in a traffic accident which happened less than two years ago, and are at least 18 years old. The main study parameter is the increase of empowerment within the intervention group compared to the control group. Empowerment will be measured by the mastery scale and a self-efficacy scale. The secondary outcomes are perceived justice, burden, well being, work ability, knowledge, amount of damages, and lawyer-client communication. Data are collected at baseline (T0 measurement before randomization), at three months, six months, and twelve months after baseline. Analyses will be conducted according to the intention-to-treat principle. Discussion This study evaluates the effectiveness of an internet intervention aimed at empowerment of PI victims. The results will give more insight into the impact of compensation proceedings on health over time, and they can have important consequences for legal claims settlement. Strengths and limitations of this study are discussed. Trial registration Netherlands Trial Register NTR236

Identification of impurities of phosphate and brominated flame retardants

In the last years, new flame retardants (FRs) have been identified for the first time in products and in environmental samples by using mainly liquid chromatography coupled to high resolution mass spectrometry. For example, 2,2-bis(chloromethyl)propane-1,3-diyl-tetrakis(2-chloroethyl)bis(phosphate), known commercially as “V6”,1 or a triazine-based flame retardant [2,4,6-tris(2,4,6-tribromophenoxy)-1,3,5-triazine, TTBP-TAZ]2 were recently reported. Not only FRs, but also their byproducts, impurities or degradation products have been very recently identified, such as those derived from tetrabromobisphenol-A (TBBP-A) or tetrabromobisphenol-S.3,4 The persistency and toxicity of these impurities or related compounds, as well as their presence in the environment is still largely unknown. We present and discuss here an overview of our results on the investigations of impurities and degradation products of FRs, namely from TTBP-TAZ2, RDP5, TBBPA and TBBPA-based products6 and the impurity diphenyl phosphate (DPHP) that derives from a variety of phosphorus flame retardants (PFRs

Identification of impurities of phosphate and brominated flame retardants

In the last years, new flame retardants (FRs) have been identified for the first time in products and in environmental samples by using mainly liquid chromatography coupled to high resolution mass spectrometry. For example, 2,2-bis(chloromethyl)propane-1,3-diyl-tetrakis(2-chloroethyl)bis(phosphate), known commercially as “V6”,1 or a triazine-based flame retardant [2,4,6-tris(2,4,6-tribromophenoxy)-1,3,5-triazine, TTBP-TAZ]2 were recently reported. Not only FRs, but also their byproducts, impurities or degradation products have been very recently identified, such as those derived from tetrabromobisphenol-A (TBBP-A) or tetrabromobisphenol-S.3,4 The persistency and toxicity of these impurities or related compounds, as well as their presence in the environment is still largely unknown. We present and discuss here an overview of our results on the investigations of impurities and degradation products of FRs, namely from TTBP-TAZ2, RDP5, TBBPA and TBBPA-based products6 and the impurity diphenyl phosphate (DPHP) that derives from a variety of phosphorus flame retardants (PFRs

Improving Performance in Combinatorial Optimization Problems with Inequality Constraints: An Evaluation of the Unbalanced Penalization Method on D-Wave Advantage

Combinatorial optimization problems are one of the target applications of current quantum technology, mainly because of their industrial relevance, the difficulty of solving large instances of them classically, and their equivalence to Ising Hamiltonians using the quadratic unconstrained binary optimization (QUBO) formulation. Many of these applications have inequality constraints, usually encoded as penalization terms in the QUBO formulation using additional variables known as slack variables. The slack variables have two disadvantages: (i) these variables extend the search space of optimal and suboptimal solutions, and (ii) the variables add extra qubits and connections to the quantum algorithm. Recently, a new method known as unbalanced penalization has been presented to avoid using slack variables. This method offers a trade-off between additional slack variables to ensure that the optimal solution is given by the ground state of the Ising Hamiltonian, and using an unbalanced heuristic function to penalize the region where the inequality constraint is violated with the only certainty that the optimal solution will be in the vicinity of the ground state. This work tests the unbalanced penalization method using real quantum hardware on D-Wave Advantage for the traveling salesman problem (TSP). The results show that the unbalanced penalization method outperforms the solutions found using slack variables and sets a new record for the largest TSP solved with quantum technology.Comment: 8 pages, 7 figures, conferenc