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Petia Radeva, Judit Martinez, A. Tovar, X. Binefa, Jordi Vitria, & Juan J. Villanueva. (1999). CORKIDENT: an automatic vision system for real-time inspection of natural products.
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Cristina Sanchez Montes, F. Javier Sanchez, Jorge Bernal, Henry Cordova, Maria Lopez Ceron, Miriam Cuatrecasas, et al. (2019). Computer-aided Prediction of Polyp Histology on White-Light Colonoscopy using Surface Pattern Analysis. END - Endoscopy, 51(3), 261–265.
Abstract: Background and study aims: To evaluate a new computational histology prediction system based on colorectal polyp textural surface patterns using high definition white light images.
Patients and methods: Textural elements (textons) were characterized according to their contrast with respect to the surface, shape and number of bifurcations, assuming that dysplastic polyps are associated with highly contrasted, large tubular patterns with some degree of bifurcation. Computer-aided diagnosis (CAD) was compared with pathological diagnosis and the diagnosis by the endoscopists using Kudo and NICE classification.
Results: Images of 225 polyps were evaluated (142 dysplastic and 83 non-dysplastic). CAD system correctly classified 205 (91.1%) polyps, 131/142 (92.3%) dysplastic and 74/83 (89.2%) non-dysplastic. For the subgroup of 100 diminutive (<5 mm) polyps, CAD correctly classified 87 (87%) polyps, 43/50 (86%) dysplastic and 44/50 (88%) non-dysplastic. There were not statistically significant differences in polyp histology prediction based on CAD system and on endoscopist assessment.
Conclusion: A computer vision system based on the characterization of the polyp surface in the white light accurately predicts colorectal polyp histology.
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Jorge Bernal, Nima Tajkbaksh, F. Javier Sanchez, Bogdan J. Matuszewski, Hao Chen, Lequan Yu, et al. (2017). Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge. TMI - IEEE Transactions on Medical Imaging, 36(6), 1231–1249.
Abstract: Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack
of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.
Keywords: Endoscopic vision; Polyp Detection; Handcrafted features; Machine Learning; Validation Framework
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L. Calvet, A. Ferrer, M. Gomes, A. Juan, & David Masip. (2016). Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation. CIE - Computers & Industrial Engineering, 94, 93–104.
Abstract: In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one.
Keywords: Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning
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A. Martinez, & Jordi Vitria. (2001). Clustering in Image Space for Place Recognition and Visiual Annotations for Human-Robot Interaction. IEEE Trans. on Systems, Man, and Cybernatics–Part B: Cybernetics, 31(5):669–682 (IF: 0.789).
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