Application of Artificial Intelligence Algorithms Within the Medical Context for Non-Specialized Users: the CARTIER-IA Platform

The use of advanced algorithms and models such as Machine Learning, Deep Learning and other related approaches of Artificial Intelligence have grown in their use given their benefits in different contexts. One of these contexts is the medical domain, as these algorithms can support disease detection, image segmentation and other multiple tasks. However, it is necessary to organize and arrange the different data resources involved in these scenarios and tackle the heterogeneity of data sources. This work presents the CARTIER-IA platform: a platform for the management of medical data and imaging. The goal of this project focuses on providing a friendly and usable interface to organize structured data, to visualize and edit medical images, and to apply Artificial Intelligence algorithms on the stored resources. One of the challenges of the platform design is to ease these complex tasks in a way that non-AI-specialized users could benefit from the application of AI algorithms without further training. Two use cases of AI application within the platform are provided, as well as a heuristic evaluation to assess the usability of the first version of CARTIER-IA. Year of Publication 2021 Journal International Journal of Interactive Multimedia and Artificial Intelligence Volume 6 Issue Regular Issue Number 6 Number of Pages 46-53 Date Published 06/2021 ISSN Number 1989-1660 URL https://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_5.pdf DOI 10.9781/ijimai.2021.05.005 DOI Google Scholar BibTeX EndNote X3 XML EndNote 7 XML Endnote tagged Marc RIS Attachment ijimai_6_6_5.pdf 932.11 K

Dermoscopy of pigmented lesions on mucocutaneous junction and mucous membrane

Author Posting. Copyright (c) The Authors 2009 This is the author's version of the work. It is posted here for personal use, not for redistribution. The definitive version was published in BRITISH JOURNAL OF DERMATOLOGY, volume: 161, issue: 6, pages:1255-1261. https://doi.org/10.1111/j.1365-2133.2009.09251.xBackground The dermoscopic features of pigmented lesions on the mucocutaneous junction and mucous membrane are different from those on hairy skin. Differentiation between benign lesions and malignant melanomas of these sites is often difficult. Objective To define the dermoscopic patterns of lesions on the mucocutaneous junction and mucous membrane, and assess the applicability of standard dermoscopic algorithms to these lesions. Patients and methods An unselected consecutive series of 40 lesions on the mucocutaneous junction and mucous membrane was studied. All the lesions were imaged using dermoscopy devices, analysed for dermoscopic patterns and scored with algorithms including the ABCD rule, Menzies method, 7-point checklist, 3-point checklist and the CASH algorithm. Results Benign pigmented lesions of the mucocutaneous junction and mucous membrane frequently presented a dotted-globular pattern (25%), a homogeneous pattern (25%), a fish scale-like pattern (18 center dot 8%) and a hyphal pattern (18 center dot 8%), while melanomas of these sites showed a multicomponent pattern (75%) and a homogeneous pattern (25%). The fish scale-like pattern and hyphal pattern were considered to be variants of the ring-like pattern. The sensitivities of the ABCD rule, Menzies method, 7-point checklist, 3-point checklist and CASH algorithm in diagnosing mucosal melanomas were 100%, 100%, 63%, 88% and 100%; and the specificities were 100%, 94%, 100%, 94% and 100%, respectively. Conclusion The ring-like pattern and its variants (fish scale-like pattern and hyphal pattern) are frequently observed as well as the dotted-globular pattern and homogeneous pattern in mucosal melanotic macules. The algorithms for pigmented lesions on hairy skin also apply to those on the mucocutaneous junction and mucous membrane with high sensitivity and specificity.ArticleBRITISH JOURNAL OF DERMATOLOGY. 161(6):1255-1261 (2009)journal articl

Dynamic Subcarrier Allocation for 100 Gbps, 40 km OFDMA-PONs with SLA and CoS

This paper was published in Journal of Lightwave Technology and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://www.opticsinfobase.org/jlt/issue.cfm?volume=31&issue=7 Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under lawThe quality of service of 100Gbps orthogonal frequency division multiple access passive optical networks (OFDMA-PONs) performing dynamic bandwidth allocation is evaluated. New medium access control protocols and frame formats have been developed, exhibiting hybrid OFDMA/time division multiple access scheduling, for capacity enhancement and granular bandwidth allocation. The sequential dynamic subcarrier allocation algorithms allow the network optical line terminal to grant the optical network units (ONUs) bandwidth using both status and non-status based algorithm. Simulations of a 100 Gbps network with 256 ONUs, 256 subcarriers and 40 km extended-reach demonstrate best network throughputs of 87.5 Gbps and 3 ms packet delays for high priority service classes, even at maximum ONU load. In addition, high service level agreement (SLA) ONUs exhibit 1.56 Gbps maximum capacity and 48.82 kbps granularity.Peer reviewedFinal Accepted Versio

Genetic algorithms for the scheduling in additive manufacturing

[EN] Genetic Algorithms (GAs) are introduced to tackle the packing problem. The scheduling in Additive Manufacturing (AM) is also dealt with to set up a managed market, called “Lonja3D”. This will enable to determine an alternative tool through the combinatorial auctions, wherein the customers will be able to purchase the products at the best prices from the manufacturers. Moreover, the manufacturers will be able to optimize the production capacity and to decrease the operating costs in each case.This research has been partially financed by the project: “Lonja de Impresión 3D para la Industria 4.0 y la Empresa Digital (LONJA3D)” funded by the Regional Government of Castile and Leon and the European Regional Development Fund (ERDF, FEDER) with grant VA049P17Castillo-Rivera, S.; De Antón, J.; Del Olmo, R.; Pajares, J.; López-Paredes, A. (2020). Genetic algorithms for the scheduling in additive manufacturing. International Journal of Production Management and Engineering. 8(2):59-63. https://doi.org/10.4995/ijpme.2020.12173OJS596382Ahsan, A., Habib, A., Khoda, B. (2015). Resource based process planning for additive manufacturing. Computer-Aided Design, 69, 112-125. https://doi.org/10.1016/j.cad.2015.03.006Araújo, L., Özcan, E., Atkin, J., Baumers, M., Tuck, C., Hague, R. (2015). Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks. 26th Annual International Solid Freeform Fabrication Symposium - an Additive Manufacturing Conference, 401-410.Berman, B. (2012). 3-D printing: The new industrial revolution. Business Horizons, 55: 155-162. https://doi.org/10.1016/j.bushor.2011.11.003Canellidis, V., Dedoussis, V., Mantzouratos, N., Sofianopoulou, S. (2006). Preprocessing methodology for optimizing stereolithography apparatus build performance. Computers in Industry, 57, 424-436. https://doi.org/10.1016/j.compind.2006.02.004Chergui, A., Hadj-Hamoub, K., Vignata, F. (2018). Production scheduling and nesting in additive manufacturing. Computers & Industrial Engineering, 126, 292-301. https://doi.org/10.1016/j.cie.2018.09.048Demirel, E., Özelkan, E.C., Lim, C. (2018). Aggregate planning with flexibility requirements profile. International Journal of Production Economics, 202, 45-58. https://doi.org/10.1016/j.ijpe.2018.05.001Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R., Todisco, V. (2018). A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling. International Journal of Industrial Engineering Computations, 9, 423-438. https://doi.org/10.5267/j.ijiec.2018.1.001Hopper, E., Turton, B. (1997). Application of genetic algorithms to packing problems - A Review. Proceedings of the 2nd Online World Conference on Soft Computing in Engineering Design and Manufacturing, Springer Verlag, London, 279-288. https://doi.org/10.1007/978-1-4471-0427-8_30Ikonen, I., Biles, W.E., Kumar, A., Wissel, J.C., Ragade, R.K. (1997). A genetic algorithm for packing three-dimensional non-convex objects having cavities and holes. ICGA, 591-598.Kim, K.H., Egbelu, P.J. (1999). Scheduling in a production environment with multiple process plans per job. International Journal of Production Research, 37, 2725-2753. https://doi.org/10.1080/002075499190491Lawrynowicz, A. (2011). Genetic algorithms for solving scheduling problems in manufacturing systems. Foundations of Management, 3(2), 7-26. https://doi.org/10.2478/v10238-012-0039-2Li, Q., Kucukkoc, I., Zhang, D. (2017). Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, 157-172. https://doi.org/10.1016/j.cor.2017.01.013Milošević, M., Lukić, D., Đurđev, M., Vukman, J., Antić, A. (2016). Genetic Algorithms in Integrated Process Planning and Scheduling-A State of The Art Review. Proceedings in Manufacturing Systems, 11(2), 83-88.Pour, M.A., Zanardini, M., Bacchetti, A., Zanoni, S. (2016). Additive manufacturing impacts on productions and logistics systems. IFAC, 49(12), 1679-1684. https://doi.org/10.1016/j.ifacol.2016.07.822Wilhelm, W.E., Shin, H.M. (1985). Effectiveness of Alternate Operations in a Flexible Manufacturing System. International Journal of Production Research, 23(1), 65-79. https://doi.org/10.1080/00207548508904691Xirouchakis, P., Kiritsis, D., Persson, J.G. (1998). A Petri net Technique for Process Planning Cost Estimation. Annals of the CIRP, 47(1), 427-430. https://doi.org/10.1016/S0007-8506(07)62867-4Zhang, Y., Bernard, A., Gupta, R.K., Harik, R. (2014). Evaluating the design for additive manufacturing: a process planning perspective. Procedia CIRP, 21, 144-150. https://doi.org/10.1016/j.procir.2014.03.17