Explainability of deep neural networks for MRI analysis of brain tumors

Purpose Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice. Methods In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent. Results NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN. Conclusion Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI

iRegNet: Non-rigid Registration of MRI to Interventional US for Brain-Shift Compensation using Convolutional Neural Networks

Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance

CD56 is a pathogen recognition receptor on human natural killer cells

Aspergillus (A.) fumigatus is an opportunistic fungal mold inducing invasive aspergillosis (IA) in immunocompromised patients. Although antifungal activity of human natural killer (NK) cells was shown in previous studies, the underlying cellular mechanisms and pathogen recognition receptors (PRRs) are still unknown. Using flow cytometry we were able to show that the fluorescence positivity of the surface receptor CD56 significantly decreased upon fungal contact. To visualize the interaction site of NK cells and A. fumigatus we used SEM, CLSM and dSTORM techniques, which clearly demonstrated that NK cells directly interact with A. fumigatus via CD56 and that CD56 is re-organized and accumulated at this interaction site time-dependently. The inhibition of the cytoskeleton showed that the receptor re-organization was an active process dependent on actin re-arrangements. Furthermore, we could show that CD56 plays a role in the fungus mediated NK cell activation, since blocking of CD56 surface receptor reduced fungal mediated NK cell activation and reduced cytokine secretion. These results confirmed the direct interaction of NK cells and A. fumigatus, leading to the conclusion that CD56 is a pathogen recognition receptor. These findings give new insights into the functional role of CD56 in the pathogen recognition during the innate immune response

The Timing of Daily Demand for Goods and Services – Multivariate Probit Estimates and Microsimulation Results for an Aged Population with German Time Use Diary Data

Though consumption research provides a broad spectrum of theoretical and empirical founded results, studies based on a daily focus are missing. Knowledge about the individual timing of daily demand for goods and services, opens – beyond a genuine contribution to consumption research – interesting societal and macro economic as well as individual personal and firm perspectives: it is important for an efficient timely coordination of supply and demand in the timing perspective as well as for a targeted economic, social and societal policy for a better support of the every day coordination of life. Last not least, the individual daily public and private living situations will be visible, which are of particular importance for the social togetherness in family and society. Our study contributes to the timing of daily consumption for goods and services with an empirical founded microanalysis on the basis of more than 37.000 individual time use diaries of the nationwide Time Budget Survey of the German Federal Statistical Office 2001/02. We describe the individual timing of daily demand for goods and services for important socio-demographic groups like for women and men, the economic situation with income poverty and daily working hour arrangements. The multivariate microeconometric explanation of the daily demand for goods and services is based on a latent utility maximizing approach over a day. We estimate an eight equation Multivariate/Simultaneous Probit Model, which allows the decision for multiple consumption activities in more than one time period a day. The estimates quantify effects on the timing of daily demand by individual socio-economic variables, which encompasses, personal, household, regional characteristics as well as daily working hour arrangements within a flexible labour market. The question about individual effects of an aged society on the timing of daily demand for goods and services is analyzed with our microsimulation model ServSim and a population forecast for 2020 by the German Federal Statistical Office. Main result: There are significant differences in explaining the timing of daily demand for goods compared to services on the one hand and in particular for different daily time periods. The conclusion: without the timing aspects an important and significant dimension for understanding individual consumption behaviour and their impacts on other individual living conditions would be missing