Valeur prédictive des critères du " Canadian CT Head Rule " pour le pronostic de développement des symptômes persistant au-delà de trois mois suite à un traumatisme craniocérébral léger

Le traumatisme craniocerebral (TCC) léger est un problème fréquent dans les services d'urgence. Dans la majorité des cas, les patients ne rencontrent pas de complications majeures dans leur processus de guérison. Néanmoins, selon les données probantes, 1% des patients développent des complications neurologiques graves peu après le traumatisme. Pour dépister les patients à risque de complications médicales graves, les médecins d'urgence utilisent les critères cliniques prédictifs du Canadian CT Head Rule (CCHR). D'autre part, certaines études démontrent que jusqu'à 40% des patients atteints de TCC léger peuvent développer des symptômes persistant au-delà de trois mois (SPTM). À ce jour, aucun suivi systématique de ces patients n'est néanmoins prévu et aucun indicateur fiable n'a été validé pour dépister les patients à risque de développement des SPTM. Selon certaines études, les patients présentant des lésions intracrâniennes post-traumatiques à l'imagerie radiologique ont une incidence plus élevée de SPTM que ceux qui ne présentent pas de lésions visibles. Dans le cadre de cette recherche, nous avons émis l'hypothèse que les patients présentant des critères du CCHR avaient une incidence plus élevée de SPTM. Cependant, les critères du CCHR n'ont jamais été validés pour le dépistage de ces complications. Nous avons donc effectué une étude prospective au sein du service d'urgence de l'Hôpital de l'Enfant-Jésus à Québec, dont l'objectif principal était de vérifier si les patients présentant des critères du CCHR avaient une incidence plus élevée de développer des SPTM que ceux ne présentant pas ces critères. L'objectif secondaire de cette étude était de vérifier si le nombre de critères présents était prédictif des SPTM. Les résultats de notre étude portant sur 77 cas de TCC léger ont démontré que les patients ayant des critères du CCHR n'avaient pas une incidence plus élevée de développer des symptômes persistants que les patients n'ayant pas ces critères. Cependant, nous avons observé que le critère d'âge (65 ans et plus) du CCHR est un facteur de risque significatif pour l'apparition des SPTM. Les patients âgés de 65 ans ou plus ayant subi un TCC léger ne sont pas nombreux, nous suggérons donc qu'une attention particulière soit portée à cette population de victimes de TCC légers

Immediate ROI search for 3-D medical images

The objective of this work is a scalable, real-time, visual search engine for 3-D medical images, where a user is able to select a query Region Of Interest (ROI) and automatically detect the corresponding regions within all returned images. We make three contributions: (i) we show that with appropriate off-line processing, images can be retrieved and ROIs registered in real time; (ii) we propose and evaluate a number of scalable exemplar-based image registration schemes; (iii) we propose a discriminative method for learning to rank the returned images based on the content of the ROI. The retrieval system is demonstrated on MRI data from the ADNI dataset, and it is shown that the learnt ranking function outperforms the baseline

Effect of aneurysm size on procedure-related rupture in patients with subarachnoid hemorrhage treated with coil occlusion

Objective: Procedure-related rupture is one of the most feared complications in treating patients with cerebral aneurysm. The primary aim of this study was to estimate the effect of aneurysm size on procedure-related rupture. We also estimated its effect on peri-procedural thromboembolic events. Methods: This observational study was conducted using routinely-collected health data on patients admitted for subarachnoid hemorrhage and treated with aneurysm coil occlusion in the CHU de Québec — Enfant-Jésus hospital from January 1st, 2000 until sample size was reached. Patients were identified from the Discharge Abstract Database using the Canadian Classification of Health codes. Assessment of complications was blind to aneurysm size. Logistic regression models were performed to test associations between aneurysm size and procedure-related rupture or peri-procedural thromboembolic events, and between both procedure-related rupture and thromboembolic events and patients' outcomes. Results: This study included 532 aneurysms treated with coil occlusion in 505 patients. Procedure-related rupture occurred in 34 patients (6.7%) and thromboembolic events in 53 (10.5%) patients. Aneurysms of 2 to 3 mm inclusively were not more significantly associated with procedure-related rupture or thromboembolic events than those larger than 3 mm (OR 1.02, 95% CI: 0.9–1.16, p = 0.78 and OR 1.06, 95% CI: 0.96–1.17, p = 0.3, respectively). However, procedure-related rupture had a significant effect on patient mortality (OR 3.86, 95% CI: 1.42–10.53, p < 0.01). Conclusions: Very small aneurysm size should not preclude aneurysm coil occlusion. Every measure should be taken to prevent procedure-related rupture as it is strongly associated with higher mortality

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards resolving this limitation by combining the two domains. In particular, we measure the success of membership inference attacks against six state-of-the-art defense methods that mitigate the risk of adversarial examples (i.e., evasion attacks). Membership inference attacks determine whether or not an individual data record has been part of a model's training set. The accuracy of such attacks reflects the information leakage of training algorithms about individual members of the training set. Adversarial defense methods against adversarial examples influence the model's decision boundaries such that model predictions remain unchanged for a small area around each input. However, this objective is optimized on training data. Thus, individual data records in the training set have a significant influence on robust models. This makes the models more vulnerable to inference attacks. To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions. We also propose two new inference methods that exploit structural properties of robust models on adversarially perturbed data. Our experimental evaluation demonstrates that compared with the natural training (undefended) approach, adversarial defense methods can indeed increase the target model's risk against membership inference attacks.Comment: ACM CCS 2019, code is available at https://github.com/inspire-group/privacy-vs-robustnes

Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction

Explaining recommendations enables users to understand whether recommended items are relevant to their needs and has been shown to increase their trust in the system. More generally, if designing explainable machine learning models is key to check the sanity and robustness of a decision process and improve their efficiency, it however remains a challenge for complex architectures, especially deep neural networks that are often deemed "black-box". In this paper, we propose a novel formulation of interpretable deep neural networks for the attribution task. Differently to popular post-hoc methods, our approach is interpretable by design. Using masked weights, hidden features can be deeply attributed, split into several input-restricted sub-networks and trained as a boosted mixture of experts. Experimental results on synthetic data and real-world recommendation tasks demonstrate that our method enables to build models achieving close predictive performances to their non-interpretable counterparts, while providing informative attribution interpretations.Comment: 14th ACM Conference on Recommender Systems (RecSys '20

DeVLBert: Learning Deconfounded Visio-Linguistic Representations

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correlations and hurt the generalization ability when transferred to out-of-domain downstream tasks. By spurious correlation, we mean that the conditional probability of one token (object or word) given another one can be high (due to the dataset biases) without robust (causal) relationships between them. To mitigate such dataset biases, we propose a Deconfounded Visio-Linguistic Bert framework, abbreviated as DeVLBert, to perform intervention-based learning. We borrow the idea of the backdoor adjustment from the research field of causality and propose several neural-network based architectures for Bert-style out-of-domain pretraining. The quantitative results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and Visual Question Answering, show the effectiveness of DeVLBert by boosting generalization ability.Comment: 10 pages, 4 figures, to appear in ACM MM 2020 proceeding

Circulating estradiol and its biologically active metabolites in endometriosis and in relation to pain symptoms

ObjectivesEndometriosis (EM) is an estrogen-dominant inflammatory disease linked to infertility that affects women of reproductive age. EM lesions respond to hormonal signals that regulate uterine tissue growth and trigger inflammation and pain. The objective of this study was to evaluate whether estradiol (E2) and its biologically active metabolites are differentially associated with EM given their estrogenic and non-estrogenic actions including proliferative and inflammatory properties.DesignWe performed a retrospective study of 209 EM cases and 115 women without EM.MethodsPain-related outcomes were assessed using surveys with validated scales. Preoperative serum levels of estradiol (E2) and estrone (E1), their 2-, 4- and 16- hydroxylated (OH) and methylated (MeO) derivatives (n=16) were measured by mass spectrometry. We evaluated the associations between estrogen levels and EM anatomic sites, surgical stage, risk of EM, and symptoms reported by women. Spearman correlations established the relationships between circulating steroids.ResultsOf the sixteen estrogens profiled, eleven were detected above quantification limits in most individuals. Steroids were positively correlated, except 2-hydroxy 3MeO-E1 (2OH-3MeO-E1). Higher 2OH-3MeO-E1 was linked to an increased risk of EM (Odd ratio (OR)=1.91 (95%CI 1.09-3.34); P=0.025). Ovarian EM cases displayed enhanced 2-hydroxylation with higher 2MeO-E1 and 2OH-E1 levels (P&lt; 0.009). Abdominal, pelvic and back pain symptoms were also linked to higher 2OH-3MeO-E1 levels (OR=1.86; 95%CI 1.06-3.27; P=0.032).ConclusionsThe 2-hydroxylation pathway emerges as an unfavorable feature of EM, and is associated with ovarian EM and pain related outcomes