Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision.

Most recent computer vision tasks take into account the distribution of image features to obtain more powerful models and better performance. One of the most commonly used techniques to this purpose is the diffusion algorithm, which fuses manifold data and k-Nearest Neighbors (kNN) graphs. In this paper, we describe how we optimized diffusion in an image retrieval task aimed at mobile vision applications, in order to obtain a good trade-off between computation load and performance. From a computational efficiency viewpoint, the high complexity of the exhaustive creation of a full kNN graph for a large database renders such a process unfeasible on mobile devices. From a retrieval performance viewpoint, the diffusion parameters are strongly task-dependent and affect significantly the algorithm performance. In the method we describe herein, we tackle the first issue by using approximate algorithms in building the kNN tree. The main contribution of this work is the optimization of diffusion parameters using a genetic algorithm (GA), which allows us to guarantee high retrieval performance in spite of such a simplification. The results we have obtained confirm that the global search for the optimal diffusion parameters performed by a genetic algorithm is equivalent to a massive analysis of the diffusion parameter space for which an exhaustive search would be totally unfeasible. We show that even a grid search could often be less efficient (and effective) than the GA, i.e., that the genetic algorithm most often produces better diffusion settings when equal computing resources are available to the two approaches. Our method has been tested on several publicly-available datasets: Oxford5k, ROxford5k, Paris6k, RParis6k, and Oxford105k, and compared to other mainstream approaches.

MetalGAN: Multi-domain label-less image synthesis using cGANs and meta-learning.

Image synthesis is currently one of the most addressed image processing topic in computer vision and deep learning fields of study. Researchers have tackled this problem focusing their efforts on its several challenging problems, e.g. image quality and size, domain and pose changing, architecture of the networks, and so on. Above all, producing images belonging to different domains by using a single architecture is a very relevant goal for image generation. In fact, a single multi-domain network would allow greater flexibility and robustness in the image synthesis task than other approaches. This paper proposes a novel architecture and a training algorithm, which are able to produce multi-domain outputs using a single network. A small portion of a dataset is intentionally used, and there are no hard-coded labels (or classes). This is achieved by combining a conditional Generative Adversarial Network (cGAN) for image generation and a Meta-Learning algorithm for domain switch, and we called our approach MetalGAN. The approach has proved to be appropriate for solving the multi-domain label-less problem and it is validated on facial attribute transfer, using CelebA dataset.

Adjustable bulbourethral male sling: Experience after 30 cases of moderate to severe male stress urinary incontinence.

OBJECTIVE: To report our experience using the Argus perineal sling from July 2015 to April 2018 for male stress urinary incontinence (SUI) after prostatic surgery. To evaluate the safety, efficacy and healthrelated quality of life in patients undergoing this procedure. PATIENTS AND METHODS: The positioning of an adjustable bulbourethral male sling provides a perineal incision, exposure of the bulbospongiosus muscle and the application of the sling bearing on it with transobturator passage of the two extremities with out-in technique. To modulate the bearing tension on the urethra, with a rigid cystoscope the Retrogade Leak Point Pressure is measured, increasing it by 10-15 cm of H20 from baseline. We retrospectively evaluated the results of this implant performed by the same operator on 30 patients who presented post-operative SUI from medium to severe (> = 2 pads/day, pad test at one hour > = 11 g). Mean operative time and possible intra and postoperative complications were evaluated. Postoperatively each patient was reassessed according to the following parameters: number of pads consumed/ die, pad tesy at one hour, ICQS-F, any related side effects. RESULTS: After the intervention, 21 of 30 patients (70% of the total) were totally continent (< 1 pad / day, pad test at 1 h < 1-2 g, ICQS-F < 11), out of them 4 required a single adjustment at 3 months in order to achieve this result. 9 of 30 patients (30 %) achieved a clinically significant improvement without obtaining total continence (mean reduction of the n° pads/day: -2.5 ± 1 DS; average reduction of the pad test at 1 h: -20 g ± 4 DS; ICQS-F average reduction: -6 points ± 2 DS), out of them 5 required a 3 month adjustment to obtain these improvements resulting, 4 needed 2 adjustments resulting because the first adjustment was not satisfactory and one who ameliorated from severe to moderate incontinence decided to live in this clinical condition. CONCLUSIONS: The results of our study show that the positioning of this sling represents a valid treatment for the moderate and severe post-surgical male SUI. The possibility of adjusting the tension of the sleeve in a "second look" makes the intervention adaptable according to the results obtained. Only multicentric clinical trials on larger series would clarify and eventually confirm the clinical benefits of this sling in post-surgical male SUI.

Role of Artificial Intelligence in Fighting Antimicrobial Resistance in Pediatrics.

Artificial intelligence (AI) is a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior. AI is extremely useful in many human activities including medicine. The aim of our narrative review is to show the potential role of AI in fighting antimicrobial resistance in pediatric patients. We searched for PubMed articles published from April 2010 to April 2020 containing the keywords "artificial intelligence", "machine learning", "antimicrobial resistance", "antimicrobial stewardship", "pediatric", and "children", and we described the different strategies for the application of AI in these fields. Literature analysis showed that the applications of AI in health care are potentially endless, contributing to a reduction in the development time of new antimicrobial agents, greater diagnostic and therapeutic appropriateness, and, simultaneously, a reduction in costs. Most of the proposed AI solutions for medicine are not intended to replace the doctor's opinion or expertise, but to provide a useful tool for easing their work. Considering pediatric infectious diseases, AI could play a primary role in fighting antibiotic resistance. In the pediatric field, a greater willingness to invest in this field could help antimicrobial stewardship reach levels of effectiveness that were unthinkable a few years ago.

Right open nephrectomy under combined spinal and peridural operative anesthesia and analgesia (CSE): A new anesthetic approach in abdominal surgery.

A case of right open nephrectomy performed under combined spinal and epidural anesthesia and analgesia was presented. This new anesthetic technique gives significant advantages to the patient by avoiding endotracheal intubation with mechanical ventilation and curare administration and by reducing the use of opioids.

Large-Scale Image Geo-Localization Using Dominant Sets.

This paper presents a new approach for the challenging problem of geo-localization using image matching in a structured database of city-wide reference images with known GPS coordinates. We cast the geo-localization as a clustering problem of local image features. Akin to existing approaches to the problem, our framework builds on low-level features which allow local matching between images. For each local feature in the query image, we find its approximate nearest neighbors in the reference set. Next, we cluster the features from reference images using Dominant Set clustering, which affords several advantages over existing approaches. First, it permits variable number of nodes in the cluster, which we use to dynamically select the number of nearest neighbors for each query feature based on its discrimination value. Second, this approach is several orders of magnitude faster than existing approaches. Thus, we obtain multiple clusters (different local maximizers) and obtain a robust final solution to the problem using multiple weak solutions through constrained Dominant Set clustering on global image features, where we enforce the constraint that the query image must be included in the cluster. This second level of clustering also bypasses heuristic approaches to voting and selecting the reference image that matches to the query. We evaluate the proposed framework on an existing dataset of 102k street view images as well as a new larger dataset of 300k images, and show that it outperforms the state-of-the-art by 20 and 7 percent, respectively, on the two datasets.

Bayesian-competitive consistent labeling for people surveillance.

This paper presents a novel and robust approach to consistent labeling for people surveillance in multi-camera systems. A general framework scalable to any number of cameras with overlapped views is devised. An off-line training process automatically computes ground-plane homography and recovers epipolar geometry. When a new object is detected in any one camera, hypotheses for potential matching objects in the other cameras are established. Each of the hypotheses is evaluated using a prior and likelihood value. The prior accounts for the positions of the potential matching objects, while the likelihood is computed by warping the vertical axis of the new object on the field of view of the other cameras and measuring the amount of match. In the likelihood, two contributions (forward and backward) are considered so as to correctly handle the case of groups of people merged into single objects. Eventually, a maximum-a-posteriori approach estimates the best label assignment for the new object. Comparisons with other methods based on homography and extensive outdoor experiments demonstrate that the proposed approach is accurate and robust in coping with segmentation errors and in disambiguating groups.

Adjuvant low-dose interleukin-2 (IL-2) plus interferon-α (IFN-α) in operable renal cell carcinoma (RCC): a phase III, randomized, multicentre trial of the Italian Oncology Group for Clinical Research (GOIRC).

There is currently no standard therapy to reduce the recurrence rate after surgery for renal cell carcinoma (RCC). The aim of this study was to assess efficacy and safety of adjuvant treatment with low doses of interleukin-2 (IL-2)+interferon-α (IFN-α) in operable RCC. The patients were randomized 1:1 to receive a 4-week cycle of low-dose IL-2+IFN-α or observation after primary surgery for RCC. Treatment cycles were repeated every 4 months for the first 2 years and every 6 months for the subsequent 3 years. The primary endpoint was recurrence-free survival (RFS); safety; and overall survival (OS) were secondary endpoints. ClinicalTrials.gov registration number was NCT00502034. 303/310 randomized patients (156 in the immunotherapy arm and 154 in the observation group) were evaluable at the intention-to-treat analyses. The 2 arms were well balanced. At a median follow-up of 52 months (range, 12-151 mo), RFS, and OS were similar, with an estimated hazard ratio (HR) of 0.84 [95% confidence interval (CI), 0.54-1.31; P=0.44] and of 1.07 (95% CI, 0.64-1.79; P=0.79), respectively in the 2 groups. Unplanned, subgroup analysis showed a positive effect of the treatment for patients with age 60 years and younger, pN0, tumor grades 1-2, and pT3a stage. Among patients with the combined presence of ≥ 2 of these factors, immunotherapy had a positive effect on RFS (HR=0.44; 95% CI, 0.24-0.82; P ≤ 0.01), whereas patients with <2 factors in the treatment arm exhibited a significant poorer OS (HR=2.27; 95% CI, 1.03-5.03 P=0.037). Toxicity of immunotherapy was mild and limited to World Health Organization grade 1-2 in most cases. Adjuvant immunotherapy with IL-2+IFN-α showed no RFS or OS improvement in RCC patients who underwent radical surgery. The results of subset analysis here presented are only hypothesis generating.

Multistage particle windows for fast and accurate object detection.

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.

[PLFT and TUMT long-term clinical results in Italy]. / Risultati a lungo termine del trattamento dell'adenoma prostatico mediante TUMT-PLFT.

Microwaves are electromagnetic radiations with wavelengths ranging from 300 MHz (0.3 GHz) and 300 GHz. Microwaves treatment reduces prostate tissue using the heat produced by microwaves. When the tissue absorbs the energy from the microwaves it coagulates and tissue necrosis occours. During hyperthermia the intraprostatic temperatures reached 40-45 °C with symptomatic improvement due to destruction of the alpha-adrenergic nerve fibers around the prostate. The term 'thermotherapy' was therefore coined to describe treatment temperatures above 45°C and hyperthermia for those below this level. Unlike thermotherapy, high-energy (thermotherapy) can achieve temperatures greater than 70°C (158°F), causing thermoablation of prostatic tissue. The authors report their experience with PLFT over a period of 8 years. They describe the technique of the treatment, the goal and its results. Many of these patients, however, were older, had a larger prostate volume, and had more surgical comorbidities, making this subset more likely to benefit from a minimally invasive option. With PLFT-TUMT, patients have a less-invasive therapy, with a catheter-free rate of 82-91% in selected patients, although the majority must also continue medical therapy.