Small drones are a rising threat due to their possible misuse for illegal activities, in particular smuggling and terrorism. The project SafeShore, funded by the European Commission under the Horizon 2020 program, has launched the "drone-vs-bird detection challenge" to address one of the many technical issues arising in this context. The goal is to detect a drone appearing at some point in a video where birds may be also present: the algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. This paper reports on the challenge proposal, evaluation, and results 1 .

The popularity of Unmanned Aerial Vehicles (UAVs) is increasing year by year and reportedly their applications hold great shares in global technology market. Yet, since UAVs can be also used for illegal actions, this raises various security issues that needs to be encountered. Towards this end, UAV detection systems have emerged to detect and further anticipate inimical drones. A very significant factor is the maximum detection range in which the system's senses can "see" an upcoming UAV. For those systems that employ optical cameras for detecting UAVs, the main issue is the accurate drone detection when it fades away into sky. This work proposes the incorporation of Super-Resolution (SR) techniques in the detection pipeline, to increase its recall capabilities. A deep SR model is utilized prior to the UAV detector to enlarge the image by a factor of 2. Both models are trained in an end-to-end manner to fully exploit the joint optimization effects. Extensive experiments demonstrate the validity of the proposed method, where potential gains in the detector's recall performance can reach up to 32.4%.

No abstract

No abstract

study included 400 patients (15 centers). The median age was 69 years (47-85), median PSA was 7.32 ng/mL (1.41-19.98), median prostate volume was 35 cc (7.4-131), median number of target biopsies per patient was 1 (1-4), and median biopsy time was 9 minutes (5-41). CsPC detection rates of patients' target and systematic biopsies were 59.3% and 49%, respectively (p<0.0001). In the pathological findings of target and systematic biopsy cores, csPC detection (57% vs. 10.4%, p<0.0001), cancer core length (6 mm vs. 3 mm, p<0.0001), and cancer core percent (38% vs. 18%, p<0.0001) were significantly higher in target biopsies than in systematic biopsies. On multivariable logistic regression analysis to predict csPC, age (OR 1.054, P[0.002), PSA value (OR 1.097, p[0.033), PSA density (OR 2.068, p<0.0001), highest PI-RADS category (OR 5.105, p<0.0001), and location of the lesion with highest PI-RADS category (TZ vs. PZ) (OR 0.488, p[0.004) were significant risk factors for detecting csPC. On multivariable logistic regression analysis to predict csPC in patients with PI-RADS categories 2 and 3, PSAD was a significant risk factor for detecting csPC (OR 3.665, p<0.0001). Areas under the receiver operating characteristic curve (AUC) for PSAD was significantly greater than non-discrimination for the detection of csPC in patients with PI-RADS categories 2 and 3 (AUC 0.751, p<0.0001). Using cut-off value: 0.150 ng/mL/cc, sensitivity, specificity, positive predictive value, and negative predictive value for detecting csPC were 92.5%, 45.3%, 41.6%, and 93.5%, respectively.CONCLUSIONS: MRI-TRUS fusion image-guided target biopsy was superior to systematic biopsy in detecting patients with csPC. Using a cut-off value of PSAD would avoid unnecessary biopsies for regions with PI-RADS categories 2 and 3.

No abstract

No abstract

An unsupervised nuclei segmentation method for histology images is proposed in this work. It consists of three modules applied to each of non-overlapping blocks: 1) data-driven color transform for dimension reduction, 2) fully-automated adaptive binarization, and 3) incorporation of geometric priors with morphological processing. The method is called CBM, which comes from the first letter of the three modules -"Color transform", "Binarization" and "Morphological processing". Experiments on the MoNuSeg dataset validate the effectiveness of the proposed CBM method. It outperforms all other unsupervised methods and offers a competitive standing among supervised models based on the Aggregated Jaccard Index (AJI) metric.