Dye diffusion during laparoscopic tubal patency tests may suggest a lymphatic contribution to dissemination in endometriosis: A prospective, observational study

Aim Women with adenomyosis are at higher risk of endometriosis recurrence after surgery. This study was to assess if the lymphatic vessel network drained from the uterus to near organs where endometriosis foci lied. Methods A prospective, observational study, Canadian Task Force Classification II-2, was conducted at Sacro Cuore Don Calabria Hospital, Negrar, Italy. 104 white women aged 18–43 years were enrolled consecutively for this study. All patients underwent laparoscopy for endometriosis and a tubal dye test was carried out. Results Evidence of dye dissemination through the uterine wall and outside the uterus was noted in 27 patients (26%) with adenomyosis as it permeated the uterine wall and a clear passage of the dye was shown in the pelvic lymphatic vessels regardless whether the tubes were unobstructed. Histological assessment of the uterine biopsies confirmed adenomyosis. Conclusion Adenomyosis is characterized by ectatic lymphatics that allow the drainage of intrauterine fluids (the dye and, perhaps, menstrual blood) at minimal intrauterine pressure from the uterine cavity though the lymphatic network to extrauterine organs. Certainly, this may not be the only explanation for endometriosis dissemination but the correlation between the routes of the dye drainage and location of endometriosis foci is highly suggestive

A meta-learning approach for training explainable graph neural networks

In this article, we investigate the degree of explainability of graph neural networks (GNNs). The existing explainers work by finding global/local subgraphs to explain a prediction, but they are applied after a GNN has already been trained. Here, we propose a meta-explainer for improving the level of explainability of a GNN directly at training time, by steering the optimization procedure toward minima that allow post hoc explainers to achieve better results, without sacrificing the overall accuracy of GNN. Our framework (called MATE, MetA-Train to Explain) jointly trains a model to solve the original task, e.g., node classification, and to provide easily processable outputs for downstream algorithms that explain the model's decisions in a human-friendly way. In particular, we meta-train the model's parameters to quickly minimize the error of an instance-level GNNExplainer trained on-the-fly on randomly sampled nodes. The final internal representation relies on a set of features that can be ``better'' understood by an explanation algorithm, e.g., another instance of GNNExplainer. Our model-agnostic approach can improve the explanations produced for different GNN architectures and use any instance-based explainer to drive this process. Experiments on synthetic and real-world datasets for node and graph classification show that we can produce models that are consistently easier to explain by different algorithms. Furthermore, this increase in explainability comes at no cost to the accuracy of the model

Explainable spatio-temporal Graph Neural Networks for multi-site photovoltaic energy production

In recent years, there has been a growing demand for renewable energy sources, which are inherently associated with a decentralized distribution and dependent on weather conditions. Their management and associated forecasting of produced energy are tasks of increasing complexity. Spatio-Temporal Graph Neural Networks have been applied in this context with excellent results, taking advantage of the correct integration of both topological data, defined by the distribution of the plants in the territory, and temporal data of the time series. A drawback of graph neural networks is the recurrent mechanism adopted to process the temporal part, which increases greatly the computational load of these models. Moreover, these models are formulated for real and sensitive contexts where, in addition to being accurate, the predictions must also be understandable by the human operator. For these reasons, in this paper we propose a novel explainable energy forecasting framework based on Spatio-Temporal Graph Neural Networks: the forecasting model generates predictions by processing temporal and spatial information using a spectral graph convolution and a 1D convolutional neural network respectively, then we apply a state-of-the-art explainer to them in order to produce explanations about the generation process. Our proposed method obtains predictions having better performance than previous approaches, both in terms of computational efficiency and prediction accuracy, with the possibility of interpreting them in order to understand the generation process. The novel approach based on fusion of forecasting and explainability in a single framework enables the creation of powerful and reliable systems suitable for real-world issues and challenges

Compressing deep-quaternion neural networks with targeted regularisation

In recent years, hyper-complex deep networks (such as complex-valued and quaternion-valued neural networks - QVNNs) have received a renewed interest in the literature. They find applications in multiple fields, ranging from image reconstruction to 3D audio processing. Similar to their real-valued counterparts, quaternion neural networks require custom regularisation strategies to avoid overfitting. In addition, for many real-world applications and embedded implementations, there is the need of designing sufficiently compact networks, with few weights and neurons. However, the problem of regularising and/or sparsifying QVNNs has not been properly addressed in the literature as of now. In this study, the authors show how to address both problems by designing targeted regularisation strategies, which can minimise the number of connections and neurons of the network during training. To this end, they investigate two extensions of l1and structured regularisations to the quaternion domain. In the authors' experimental evaluation, they show that these tailored strategies significantly outperform classical (realvalued) regularisation approaches, resulting in small networks especially suitable for low-power and real-time applications

Combined Sparse Regularization for Nonlinear Adaptive Filters

Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity. Recently, a class of proportionate algorithms has been proposed for nonlinear filters to leverage sparsity of their coefficients. However, the choice of the norm penalty of the cost function may be not always appropriate depending on the problem. In this paper, we introduce an adaptive combined scheme based on a block-based approach involving two nonlinear filters with different regularization that allows to achieve always superior performance than individual rules. The proposed method is assessed in nonlinear system identification problems, showing its effectiveness in taking advantage of the online combined regularization.Comment: This is a corrected version of the paper presented at EUSIPCO 2018 and published on IEEE https://ieeexplore.ieee.org/document/855295

Re-identification of objects from aerial photos with hybrid siamese neural networks

In this paper, we consider the task of re-identifying the same object in different photos taken from separate positions and angles during aerial reconnaissance, which is a crucial task for the maintenance and surveillance of critical large-scale infrastructure. To effectively hybridize deep neural networks with available domain expertise for a given scenario, we propose a customized pipeline, wherein a domain-dependent object detector is trained to extract the assets (i.e., sub-components) present on the objects, and a siamese neural network learns to re-identify the objects, exploiting both visual features (i.e., the image crops corresponding to the assets) and the graphs describing the relations among their constituting assets. We describe a real-world application concerning the re-identification of electric poles in the Italian energy grid, showing our pipeline to significantly outperform siamese networks trained from visual information alone. We also provide a series of ablation studies of our framework to underline the effect of including topological asset information in the pipeline, learnable positional embeddings in the graphs, and the effect of different types of graph neural networks on the final accuracy

Clinical and radiological criteria for the differential diagnosis between asbestosis and idiopathic pulmonary fibrosis: Application in two cases

Introduction: Idiopathic pulmonary fibrosis (IPF) and asbestosis are pulmonary interstitial diseases that may present overlapping clinical aspects in the full-blown phase of the disease. For both clinical entities the gold standard for diagnosis is histological examination, but its execution poses ethical problems, especially when performed for preventive or forensic purposes. Objective: To evaluate the application of internationally accepted clinical, anamnestic and radiological criteria for differential diagnosis between asbestosis and IPF, and to assess the ability to discriminate between the two diseases. Even if clinically similar, the two diseases present extremely different prognostic and therapeutic perspectives. Methods: Two clinical cases of IPF are reported, in which the differential diagnosis was made by studying occupational exposure to asbestos, the onset and progression of clinical symptoms, and the identification of specific radiological elements by means of chest High Resolution Computed Tomography (HRCT). Results: The diagnosis of IPF could be made on the basis of the absence of significant exposure to asbestos, the early onset and rapid progression of dyspnea and restrictive ventilatory defects, in association with a pulmonary radiological pattern characterized by peculiar elements such as honeycombing. Discussion: The diagnostic procedure adopted to make a differential diagnosis with asbestosis provides practical clinical elements facilitating the differentiation between the two forms of pulmonary fibrosis, a fundamental aspect of the activity of the occupational physician

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

Lung segmentation and characterization in covid-19 patients for assessing pulmonary thromboembolism: An approach based on deep learning and radiomics

The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature

Occurrence of Acute Pulmonary Embolism in COVID-19—A case series

[No abstract available