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Author (down) Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Vivek Kumar Singh; Syeda Furruka Banu; Forhad U H Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig; Mohamed Abdel-Nasser
Title SLSNet: Skin lesion segmentation using a lightweight generative adversarial network Type Journal Article
Year 2021 Publication Expert Systems With Applications Abbreviated Journal ESWA
Volume 183 Issue Pages 115433
Keywords
Abstract The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ SRA2021 Serial 3633
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Author (down) Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Syeda Furruka Banu; Adel Saleh; Vivek Kumar Singh; Forhad U. H. Chowdhury; Saddam Abdulwahab; Santiago Romani; Petia Radeva; Domenec Puig
Title SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. Type Conference Article
Year 2018 Publication 21st International Conference on Medical Image Computing & Computer Assisted Intervention Abbreviated Journal
Volume 2 Issue Pages 21-29
Keywords
Abstract Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU.
Address Granada; Espanya; September 2018
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Area Expedition Conference MICCAI
Notes MILAB; no proj Approved no
Call Number Admin @ si @ SRA2018 Serial 3112
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Author (down) Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig
Title Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism Type Journal Article
Year 2019 Publication IEEE Access Abbreviated Journal ACCESS
Volume 7 Issue Pages 39069-39082
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Abstract Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called “EgoFoodPlaces” that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the “EgoFoodPlaces” dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3.
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Notes MILAB; no menciona Approved no
Call Number Admin @ si @ SRA2019 Serial 3296
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Author (down) Maya Dimitrova; Petia Radeva; David Rotger; D. Boyadjiev; Juan J. Villanueva
Title Advanced Cardiological Diagnosis via Intelligent Image Analysis Type Miscellaneous
Year 2004 Publication Abbreviated Journal
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Address Varna (Bulgaria)
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ DRR2004 Serial 477
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Author (down) Maya Dimitrova; N. Kushmerick; Petia Radeva; Juan J. Villanueva
Title User Assesment of a Visual Genre Classifier Type Miscellaneous
Year 2003 Publication Proceedings of the 3rd IASTED Int. Conference Visualization, Imaging and Image Processing Abbreviated Journal
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ DKR2003 Serial 372
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Author (down) Maya Dimitrova; I. Terziev; Petia Radeva; Juan J. Villanueva
Title Java-Servlet Technology for Building New Web Document Classifiers Type Miscellaneous
Year 2004 Publication Abbreviated Journal
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Address Varna (Bulgaria)
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ DTR2004 Serial 476
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Author (down) Maya Dimitrova; Ch. Roumenin; Siya Lozanova; David Rotger; Petia Radeva
Title An Interface System Based on Multimodal Principle for Cardiological Diagnosis Assistance Type Conference Article
Year 2007 Publication International Conference On Computer Systems And Technologies Abbreviated Journal
Volume IIIB.4 Issue Pages 1–6
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Address Bulgaria
Corporate Author Thesis
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Area Expedition Conference CompSysTech’07
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ DRL2007 Serial 833
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Author (down) Maya Dimitrova; Ch. Roumenin; Petia Radeva; David Rotger; Juan J. Villanueva
Title Multimodal Intelligent System for Cardiovascular Diagnosis Type Miscellaneous
Year 2003 Publication Automation and Informatics, any XXXVII, num. 3 Abbreviated Journal
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ DRR2003 Serial 374
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Author (down) Maurizio Mencuccini; Jordi Martinez-Vilalta; Josep Piñol; Lasse Loepfe; Mireia Burnat ; Xavier Alvarez; Juan Camacho; Debora Gil
Title A quantitative and statistically robust method for the determination of xylem conduit spatial distribution Type Journal Article
Year 2010 Publication American Journal of Botany Abbreviated Journal AJB
Volume 97 Issue 8 Pages 1247-1259
Keywords Geyer; hydraulic conductivity; point pattern analysis; Ripley; Spatstat; vessel clusters; xylem anatomy; xylem network
Abstract Premise of the study: Because of their limited length, xylem conduits need to connect to each other to maintain water transport from roots to leaves. Conduit spatial distribution in a cross section plays an important role in aiding this connectivity. While indices of conduit spatial distribution already exist, they are not well defined statistically. * Methods: We used point pattern analysis to derive new spatial indices. One hundred and five cross-sectional images from different species were transformed into binary images. The resulting point patterns, based on the locations of the conduit centers-of-area, were analyzed to determine whether they departed from randomness. Conduit distribution was then modeled using a spatially explicit stochastic model. * Key results: The presence of conduit randomness, uniformity, or aggregation depended on the spatial scale of the analysis. The large majority of the images showed patterns significantly different from randomness at least at one spatial scale. A strong phylogenetic signal was detected in the spatial variables. * Conclusions: Conduit spatial arrangement has been largely conserved during evolution, especially at small spatial scales. Species in which conduits were aggregated in clusters had a lower conduit density compared to those with uniform distribution. Statistically sound spatial indices must be employed as an aid in the characterization of distributional patterns across species and in models of xylem water transport. Point pattern analysis is a very useful tool in identifying spatial patterns.
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Notes IAM; Approved no
Call Number IAM @ iam @ MMG2010 Serial 1623
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Author (down) Matthias S. Keil; Jordi Vitria
Title Does the brain generate representations of smooth brightness gradients? A novel account for Mach bands, Chevreul’s illusion, and a variant of the Ehrenstein disk Type Miscellaneous
Year 2005 Publication European Conference on Visual Perception Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ KeV2005b Serial 607
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Author (down) Matthias S. Keil; Jordi Vitria
Title Does the brain generate representations of smooth brightness gradients? A novel account for Mach bands, Chevreul’s illusion, and a variant of the Ehrenstein disk Type Journal
Year 2005 Publication Perception 34:209–210 Suppl. S (IF: 1.391) Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ KeV2005a Serial 608
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Author (down) Matthias S. Keil; Jordi Vitria
Title Pushing it to the Limit: Adaptation with Dynamically Switching Gain Control Type Journal
Year 2007 Publication EURASIP Journal on Advances in Signal Processing, Vol 2007, Article ID 51684, 10 pages, doi: 10.1155/2007/51684 Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ KeV2007 Serial 794
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Author (down) Matthias S. Keil; Gabriel Cristobal; Thorsten Hansen; Heiko Neumann
Title Recovering real-world images from single-scale boundaries with a novel filling-in architecture Type Journal
Year 2005 Publication Neural Networks 18(10):1319–1331 (IF: 1.665) Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ KCH2005 Serial 576
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Author (down) Matthias S. Keil; Gabriel Cristobal; Heiko Neumann
Title Gradient representation and perception in the early visual system – A novel account of Mach band formation Type Journal
Year 2006 Publication Vision Research, 46(17): 2659–2674 Abbreviated Journal VR
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Notes Approved no
Call Number Admin @ si @ KCN2006 Serial 649
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Author (down) Matthias S. Keil; Gabriel Cristobal
Title Separating the chaff from the wheat: possible origins of the oblique effect Type Journal
Year 2000 Publication Journal of the Optical Society of America A – Optics, Image Science, and Vision, 17(4): 697–710 (IF: 1.481) Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ KeC2000 Serial 630
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