Single injection dual phase CBCT technique ameliorates results of trans-arterial chemoembolization for hepatocellular cancer

Cone-beam CT (CBCT) application to the field of trans-arterial chemoembolization has been recently the focus of several researches. This imaging modality is performed with a rotation of the C-arm around the patient, without needs of patient repositioning. Datasets are immediately processed, obtaining volumetric CT-like images with the possibility of post-processing and reconstruction of images. Dual phase CBCT recently introduced in clinical practice consists in a first arterial acquisition followed by a delayed acquisition corresponding to a venous phase. The introduction of this feature has overcome the limit of single-phase acquisitions, allowing lesions characterization. Moreover these recent advantages have several intra-procedural implications. Detailed technical and acquisition parameters will be widely exposed in this review with particular attention to: catheter positioning, acquisition delay, injection parameters, patient positioning and contrast dilution. Comparison with standard of practice second line imaging [multidetector computer tomography (MDCT) and MDCT/arteriography] demonstrate the capability of detecting occult nodules providing some clinical implications thus potentially identifying a sub set of patients with aggressive disease behaviour. Other intra-procedural advantages of dual phase CBCT usage consist in a better tumor feeder visualization, reduction of proper DSA and fluoroscopic time, suggestion the presence of an extrahepatic parasitic feeder thus resulting in a more accurate treatment. Finally, the volumetrical intraprocedural evaluation of accumulation of embolic agent has proved to be correlate with treatment response if compared with MRI

What do faculties specializing in brain and neural sciences think about, and how do they approach, brain-friendly teaching-learning in Iran?

Objective: to investigate the perspectives and experiences of the faculties specializing in brain and neural sciences regarding brain-friendly teaching-learning in Iran. Methods: 17 faculties from 5 universities were selected by purposive sampling (2018). In-depth semi-structured interviews with directed content analysis were used. Results: 31 sub-subcategories, 10 subcategories, and 4 categories were formed according to the “General teaching model”. “Mentorship” was a newly added category. Conclusions: A neuro-educational approach that consider the roles of the learner’s brain uniqueness, executive function facilitation, and the valence system are important to learning. Such learning can be facilitated through cognitive load considerations, repetition, deep questioning, visualization, feedback, and reflection. The contextualized, problem-oriented, social, multi-sensory, experiential, spaced learning, and brain-friendly evaluation must be considered. Mentorship is important for coaching and emotional facilitation

Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks

Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained. In this paper, we introduce a structured approach for generating Universal Adversarial Perturbations (UAPs) with procedural noise functions. Our approach unveils the systemic vulnerability of popular DCN models like Inception v3 and YOLO v3, with single noise patterns able to fool a model on up to 90% of the dataset. Procedural noise allows us to generate a distribution of UAPs with high universal evasion rates using only a few parameters. Additionally, we propose Bayesian optimization to efficiently learn procedural noise parameters to construct inexpensive untargeted black-box attacks. We demonstrate that it can achieve an average of less than 10 queries per successful attack, a 100-fold improvement on existing methods. We further motivate the use of input-agnostic defences to increase the stability of models to adversarial perturbations. The universality of our attacks suggests that DCN models may be sensitive to aggregations of low-level class-agnostic features. These findings give insight on the nature of some universal adversarial perturbations and how they could be generated in other applications.Comment: 16 pages, 10 figures. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19