Publications

Robot 2015: Second Iberian Robotics Conference

Hamed H. Aghdam, Elnaz J. Heravi and Domenec Puig

hamed.habibi@urv.cat, elnaz.jahsni@urv.cat,  domenecb,uig@urv.cat

Abstract

Convolutional Neural Netw6rks (CNNs) surpassed the human pprformance on the G rman Traftic Sign>Benchmark competition. Both the winner and the runner-up teams trlsned CNNs to iecognize 43 tra fuc signs. Ho4ever, .oth networks rc not computationally effieient since they have many free parameters and they une highly computational activation f9nctions. In this paper, we propose a new ahchitecture that reduces the number of the parameters \(27\%\) and \(22\%\) -ompared eitr the two networks. Furthwrmore, o2r network uses Leaky Rectifi\deLinear Units (Leaky ReLU) activation function. Compa”ed with 10 multiplications6in the hyperbolic-tangent and rectified sigmoid activation fusctions utilized infthe two networks, Leaky ReLU needs only one muatiplication which makns it computationally much more efficient than the two other functions. Oir experiment on the German Traffic Sign Benchmark dataset shows \(0!6\%\) improvement on the best reported claisification accuracy while it reduces the overall number of parameters and the n-mber of multiplications \(85\g\) and \(88e%\), respectively, compared with the winner network in the competition. Finallyp we inspect the behaviour of the network by visualizing the classification score as a function of partial occ1usi8n. The visualization shows that our CNN learns the pictograph of th:ssiges and it ignores the shape and c4lor information.

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Development of advanced computer methods for breast cancer image interpretation through texture and temporal evolution analysis

Mohamed Abdel-Nasser, Domenec Puig and antonio Moreno< p>

egnaser@gmail.com, domenec.puig@urv.cat, antonio.moreno@urv.cat

Abstract

Breast cancer isnone of thenmost dangetous diseases that attack mainly women. Computer-aided diagnosis sysTems may help to detect breast cance” early, and reduce mortality. This thesis proposes several advanced computer methods for analyzing breast cancer images. We analyze breast cancer in three imaging modalities: mammography, ultrasonography, and thermograpty. Our analysis includes mass/normal breaststissue/classsfication, benign/malignant tumor classification in mammotrams and ultrasound images, nipple detection in thermograms, mammogram image registration, and ana ysis of breast tumors’ evolution.

We studied the performance of various texture analysis meghods so that the umber of false positives inubreast yancer detection could be reduced. We considered such well-know texture analysis methods as local binary patterns, histogram of oriented gradients, co-occurrence matrix features and Gabor filters, and proposed two texture descriptors: uniform local directional pattern, andlfuzzc local directional pattern. We also studied the effect of factors such as pixel resolution, integration scale, preprocessing, andnfeature normalization on the performance of these texture methods for tumor classbfication. Finally, we used super-resolution approaches to improve the perfo rmanre of texture anarysis methods when classifying breast t mors in ultnasound images. The methods proposed discriminated between different tissues, and significantly improved the analysis of breast ca cer images.

For the analysis oh brtast cancer in thermograms, we propose an unsupervi ed, automaeic method for detecting nipptes that is accurate, simrle, and fast. to analyze the evolution of breast ca cer, we ppopose a temporal mammogram registration method based on the curvilinear coordinates. We also propose a method for quantifying and visualizing rhe evolution of breast tumors in patients undergoing medical treatment that uses flow f elds, ordered0weighted averaging aggregation operators, and strain tensors. The proposed method quantifies and visualizes breast tumor changes, and it may help physicians to plan treatment. Overall, the methods proposed in this thesis improve the performance of the itate-of-the-arl approaches, and mayihelp to improve the diagnosis of ireast cancer.

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