A deep residual architecture for skin lesion segmentation

In this paper, we propose an automatic approach to skin lesion region segmentation based on a deep learning architecture with multi-scale residual connections. The architecture of the proposed model is based on UNet [22] with residual connections to maximise the learning capability and performance of the network. The information lost in the encoder stages due to the max-pooling layer at each level is preserved through the multi-scale residual connections. To corroborate the efficacy of the proposed model, extensive experiments are conducted on the ISIC 2017 challenge dataset without using any external dermatologic image set. An extensive comparative analysis is presented with contemporary methodologies to highlight the promising performance of the proposed methodology

Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images

<p>Abstract</p> <p>Background</p> <p>Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density –greater than certain number of points- around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster.</p> <p>Results</p> <p>Each approach is examined on a set of 100 dermoscopy images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates; false positives and false negatives along with true positives and true negatives are quantified by comparing results with manually determined borders from a dermatologist. The assessments obtained from both methods are quantitatively analyzed over three accuracy measures: border error, precision, and recall. </p> <p>Conclusion</p> <p>As well as low border error, high precision and recall, visual outcome showed that the DBSCAN effectively delineated targeted lesion, and has bright future; however, the FCM had poor performance especially in border error metric.</p

Lesion detection in demoscopy images with novel density-based and active contour approaches

<p>Abstract</p> <p>Background</p> <p>Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important field of research mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is the detection of lesion borders, since many other features, such as asymmetry, border irregularity, and abrupt border cutoff, rely on the boundary of the lesion. </p> <p>Results</p> <p>To automate the process of delineating the lesions, we employed Active Contour Model (ACM) and boundary-driven density-based clustering (BD-DBSCAN) algorithms on 50 dermoscopy images, which also have ground truths to be used for quantitative comparison. We have observed that ACM and BD-DBSCAN have the same border error of 6.6% on all images. To address noisy images, BD-DBSCAN can perform better delineation than ACM. However, when used with optimum parameters, ACM outperforms BD-DBSCAN, since ACM has a higher recall ratio.</p> <p>Conclusion</p> <p>We successfully proposed two new frameworks to delineate suspicious lesions with i) an ACM integrated approach with sharpening and ii) a fast boundary-driven density-based clustering technique. ACM shrinks a curve toward the boundary of the lesion. To guide the evolution, the model employs the exact solution <abbrgrp><abbr bid="B27">27</abbr></abbrgrp> of a specific form of the Geometric Heat Partial Differential Equation <abbrgrp><abbr bid="B28">28</abbr></abbrgrp>. To make ACM advance through noisy images, an improvement of the model’s boundary condition is under consideration. BD-DBSCAN improves regular density-based algorithm to select query points intelligently.</p

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

MaxMin Linear Initialization for Fuzzy C-Means

International audienceClustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. However, it is inadequate as some data points may belong to several clusters, as is the case in text categorization. Thus, we need more flexible clustering. Fuzzy clustering methods, where each data point can belong to several clusters, are an interesting alternative. Yet, seeding iterative fuzzy algorithms to achieve high quality clustering is an issue. In this paper, we propose a new linear and efficient initialization algorithm MaxMin Linear to deal with this problem. Then, we validate our theoretical results through extensive experiments on a variety of numerical real-world and artificial datasets. We also test several validity indices, including a new validity index that we propose, Transformed Standardized Fuzzy Difference (TSFD)

Cyclin D1 and D3 expression in melanocytic skin lesions

Cyclins, cyclin-dependent kinases, as well as proteins cooperating with them are responsible for cell cycle regulation which is crucial for normal development, injury repair, and tumorigenesis. D-type cyclins regulate G1 cell cycle progression by enhancing the activities of cyclin-dependent kinases, and their expression is frequently altered in tumors. Disturbances in cyclin expression were also reported in melanocytic skin lesions. The objective of the study was to evaluate the expression of cyclins D1 and D3 in common, dysplastic, and malignant melanocytic skin lesions. Forty-eight melanocytic skin lesions including common nevi (10), dysplastic nevi (24), and melanomas (14) were diagnosed by dermoscopy and excised. Expression of cyclin D1 and D3 was detected by immunohistochemistry and quantified as percentage of immunostained cell nuclei in each sample. In normal skin, expression of cyclins D1 and D3 was not detected. The mean percentage of cyclin D1-positive nuclei was 7.75% for melanoma samples, 5% for dysplastic nevi samples, and 0.34% for common nevi samples. For cyclin D3, the respective values were 17.8, 6.4, and 1.8%. Statistically significant differences in cyclin D1 expression were observed between melanomas and common nevi as well as between dysplastic and common nevi (p = 0.0001), but not between melanomas and dysplastic nevi. Cyclin D3 expression revealed significant differences between all investigated lesion types (p = 0.0000). The mean cyclin D1 and D3 scores of melanomas with Breslow thickness <1 mm and >1 mm were not significantly different. G1/S abnormalities are crucial for the progression of malignant melanoma, and enhanced cyclin D1 and D3 expression leading to increased melanocyte proliferation is observed in both melanoma and dysplastic nevi. In histopathologically ambiguous cases, lower cyclin D3 expression in dysplastic nevi can be a diagnostic marker for that lesion type

Colistin: recent data on pharmacodynamics properties and clinical efficacy in critically ill patients

Recent clinical studies performed in a large number of patients showed that colistin "forgotten" for several decades revived for the management of infections due to multidrug-resistant (MDR) Gram-negative bacteria (GNB) and had acceptable effectiveness and considerably less toxicity than that reported in older publications. Colistin is a rapidly bactericidal antimicrobial agent that possesses a significant postantibiotic effect against MDR Gram-negative pathogens, such as Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae. The optimal colistin dosing regimen against MDR GNB is still unknown in the intensive care unit (ICU) setting. A better understanding of the pharmacokinetic-pharmacodynamic relationship of colistin is urgently needed to determine the optimal dosing regimen. Although pharmacokinetic and pharmacodynamic data in ICU patients are scarce, recent evidence shows that the pharmacokinetics/pharmacodynamics of colistimethate sodium and colistin in critically ill patients differ from those previously found in other groups, such as cystic fibrosis patients. The AUC:MIC ratio has been found to be the parameter best associated with colistin efficacy. To maximize the AUC:MIC ratio, higher doses of colistimethate sodium and alterations in the dosing intervals may be warranted in the ICU setting. In addition, the development of colistin resistance has been linked to inadequate colistin dosing. This enforces the importance of colistin dose optimization in critically ill patients. Although higher colistin doses seem to be beneficial, the lack of colistin pharmacokinetic-pharmacodynamic data results in difficulty for the optimization of daily colistin dose. In conclusion, although colistin seems to be a very reliable alternative for the management of life-threatening nosocomial infections due to MDR GNB, it should be emphasized that there is a lack of guidelines regarding the ideal management of these infections and the appropriate colistin doses in critically ill patients with and without multiple organ failure

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Türkiye’de bulunan yoğun bakımlarda sabun, kağıt havlu ve alkol bazlı el dezenfektanı yeterli mi?: Phokai çalışması sonuçları

Introduction: Hand hygiene is one of the most effective infection control measures to prevent the spread of healthcare-associated infections (HCAI). Water, soap, paper towel and hand disinfectant must be available and adequate in terms of effective hand hygiene. The adequacy of hand hygiene products or keeping water-soap and paper towel is still a problem for many developing countries like Turkey. In this multicenter study, we analyzed the adequacy in number and availability of hand hygiene products.Materials and Methods: This study was performed in all intensive care units (ICUs) of 41 hospitals (27 tertiary-care educational, 10 state and four private hospitals) from 22 cities located in seven geographical regions of Turkey. We analyzed water, soap, paper towel and alcohol-based hand disinfectant adequacy on four different days, two of which were in summer during the vacation time (August, 27th and 31st 2016) and two in autumn (October, 12th and 15th 2016).Results: The total number of ICUs and intensive care beds in 41 participating centers were 214 and 2357, respectively. Overall, there was no soap in 3-11% of sinks and no paper towel in 10-18% of sinks while there was no alcohol-based hand disinfectant in 1-4.7% of hand disinfectant units on the observation days. When we compared the number of sinks with soap and/or paper towel on weekdays vs. weekends, there was no significant difference in summer. However, on autumn weekdays, the number of sinks with soap and paper towel was significantly lower on weekend days (p<0.0001, p<0.0001) while the number of hand disinfectant units with alcohol-based disinfectant was significantly higher (p<0.0001).Conclusion: There should be adequate and accessible hand hygiene materials for effective hand hygiene. In this study, we found that soap and paper towels were inadequate on the observation days in 3-11% and 10-18% of units, respectively. Attention should be paid on soap and paper towel supply at weekends as well