Aula

Alnajjar, F., & Murase, W. (2008). Sensor-fusion in spiking neural network that generates autonomous behavior in real mobile robot. IEEE World Congress on Computational Intelligence-IJCNN. IEEE.
Ambard, M., Guo, B., Martinez, D., & Bermak, A. (2008). A spiking neural network for gas discrimination using a tin oxide sensor array. DELTA 2008. 4th IEEE International Symposium On Electronic Design, Test and Applications (págs. 394-397). New Jersey, USA: IEEE.
Ambrosini, V., Fanti, S., Chengazi, V., & Rubello, D. (2014). Diagnostic accuracy of FDG PET/CT in mediastinal lymph nodes from lung cancer. European Journal Radiology, 83:1301–2.
Arguello, E., Silva, R., Castillo, C., & M, H. (2012). The neuroid: A novel and simplified neuron-model. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (págs. 1234-1237). New Jersey, USA: Institute of Electrical and Electronics Engineers-IEEE.
Balleyguier, C., Ayadi, S., Van Nguyen, K., Vanel, D., & Dromain, C. (2007). Sigal R. BIRADS classification in mammography. Eur J Radiol. , 61(2):192–4.
Botzheim, J., Obo, T., & Kubota, N. (2012). Human gesture recognition for robot partners by spiking neural network and classification learning. Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference On. (págs. 1954-1958). New Jersey, USA: Institute of Electrical and Electronics Engineers-IEEE.
Bouganis, A., & Shanahan, M. (2008). Training a spiking neural network to control a 4-dof robotic arm based on spike timing-dependent plasticity. The 2010 International Joint Conference On Neural Networks (IJCNN), (págs. 2200–2206). New Jersey, USA: Institute of Electrical and Electronics Engineers-IEEE.
Broderick, S., & Meyers, B. ( 2012). PET staging of mediastinal lymph nodes in thoracic oncology. Thorac Surgery Clinical, 22:161–6.
Brooks, F., & Grigsby, P. (2015). Low-order non-spatial effects dominate second-order spatial effects in the texture quantifier analysis of 18F-FDG-PET images. Plos One, 10:1–17.
Cárdenas-Sánchez, J., Bargalló-Rocha, J., Erazo-Valle, A., Poitevin-Chacón, A., V, V.-C., & Sánchez, D. (2015). Consenso Mexicano sobre diagnóstico y tratamiento del cáncer mamario. . (pág. 117). México: Masson Doyma .
Chen, E., Chung, P., Chen, C., Tsai, H., & Chang, C. (1998). An automatic diagnostic system for ct liver image classification. . Biomed Eng IEEE Trans, 45(6):783–94.
Cheng, J., Ni, D., Chou, Y., Qin, J., Tiu, C., & Chang, Y. (2016). Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Scientific Reports. .
Chicklore, S., Goh, V., Siddique, M., Roy, A., Marsden, P., & Cook, G. (2013). Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. European Journal on Nuclear Medicine, 40:133–40.
Christoyianni, I., Koutras, A., Dermatas, E., & Kokkinakis, G. (2002). Computer aided diagnosis of breast cancer in digitized mammograms. Comput Med Imaging Graph, 26(5):309-19.
Cseh, Z., Kenny, L., Swingland, J., Bose, S., & Turheimer., F. E. (March 2013). Neural networks combined with region growing techniques for tumor detection in [18F]-fluorothymidine dynamic positron emission tomography breast cancer studies.
De Leyn, P., Vansteenkiste, J., Cuypers, P., Deneffe, G., Van Raemdonck, D., & Coosemans, W. (1997). Role of cervical mediastinoscopy in staging of non-small cell lung cancer without enlarged mediastinal lymph nodes on CT scan. European Journal on Cardiothorax Surgery, 12:706.
DeVita, V., Lawrence, T., & Rosenberg, S. (2005). Devita, Hellman, and Rosenberg's Cancer: Principles & Practice of Oncology. Lippincott Williams And Wilkins.
Edet-Sanson, A., Dubray, B., Doyeux, K., Back, A., Hapdey, S., & Modzelewski, R. ( 2012). Serial assessment of FDG-PET FDG uptake and functional volume during radiotherapy (RT) in patients with non-small cell lung cancer (NSCLC). Radiotherapy Oncology, 102:251–7.
Fang, H., Wang, Y., & He, J. (2009). Spiking neural networks for cortical neuronal spike train decoding. Neural Comput. págs. 22(4):1060–1085.
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., & Rebelo, M. (2015 ). Cancer incidence and mortality worldwide: Sources, methods and major patterns. 2012 Int Journal on Cancer. GLOBOCAN.
Fisher, R. (1936). UCI Machine Learning Repository. Recuperado el 2019, de Center for Machine Learning and Intelligent Systems: http://archive.ics.uci.edu/ml/datasets/iris
Gao, X., Chu, C., Li, Y., Lu, P., Wang, W., & Liu, W. (2015). The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from (18)F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer. European Journal Radiology, 84:312–7.
Gerstner, W., & Kistler, W. (2002). Spiking Neuron Models: Single Neurons, Populations, Plasticity. . (pág. 494). Cambridge, United Kingdom: Cambridge University Press.
Gonçalves, V., & Delamaro, M. ( 2014). A systematic review on the evaluation and characteristics of computer-aided diagnosis systems. Revista Brasileira De Engenharia Biomedica, 30:355–83.
Halkiotis, S., Botsis, T., & Rangoussi, M. ( 2007). Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks. Signal Processing, 87(7):1559–68.
Hatt, M., Tixier, F., Pierce, L., Kinahan, P., Le Rest, C., & Visvikis, D. (2016). Characterization of PET/CT images using texture analysis: the past, the present… any future? . European Journal of Nuclear Medicine and Molecular Imaging, 44(1):1-15.
Hongkai Wang, Z. Z. (2017). Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.
IBM. (2019). www.ibm.com. Recuperado el 2019, de https://www.ibm.com/products/clinical-decision-support-oncology
Ishikawa, Y., & Fukai, S. (2004). A neuron mos variable logic circuit with the simplified circuit structure. Proceedings of 2004 IEEE Asia-Pacific Conference on Advanced System Integrated Circuits 2004. New Jersey, USA: Institute of Electrical and Electronics Engineers-IEEE.
Izhikevich, E. (2003). Simple Model of Spiking Neurons. . IEEE Transactions on Neural Networks, 1569–1572.
Jain, A., & Akula, A. (2014). https://github.com. Recuperado el 2019, de Github: https://github.com/appurvj/SNNImpl
Kim, S., Allen-Auerbach, M., Goldin, J., Fueger, B., Dahlbom, M., & Brown, M. (2007). Accuracy of PET/CT in characterization of solitary pulmonary lesions. Journal on Nuclear Medicine, 48:214–20.
Kroon, D.-J. (Marzo de 2008). matlabcentral-fileexchange. Recuperado el 2019, de https://es.mathworks.com: https://es.mathworks.com/matlabcentral/fileexchange/19084-region-growing
Kumar, V., Abbas, A., Fausto, N., Aster, J., & Robbins, C. (2009). Pathologic Basis of Disease. 1065-1096.
Laurene, F. (1994). Fundamentals of Neural Networks—Architectures, Algorithms, and Applications. Prentice Hall.
Li, X., Zhang, H., Xing, L., Ma, H., Xie, P., & Zhang, L. (2012). Mediastinal lymph nodes staging by 18F-FDG PET/CT for early stage non-small cell lung cancer: a multicenter study. Radiotherapy Oncology. , 102:246–50.
Lian, C., Ruan, S., Denœux, T., Jardin, F., & Vera, P. (2016). Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Medical Image Analytics, 32:257–68.
Liao, C., Chen, J., Liang, J., Yeh, J., & Kao, C. (2012). Meta-analysis study of lymph node staging by 18F-FDG PET/CT scan in non-small cell lung cancer: comparison of TB and non-TB endemic regions. European Journal Radiology, 81:3518–23.
Lorenzo, R., Riccardo, R., & Antonio, C. (2006). A new unsupervised neural network for pattern recognition with spiking neurons. International Joint Conference on Neural Networks (págs. 3903-3910). New Jersey, USA: Institute of Electrical and Electronics Engineers-IEEE.
Lovelace, J., Rickard, J., & Cios, K. (2010). A spiking neural network alternative for the analog to digital converter. The 2010 International Joint Conference On Neural Networks (IJCNN) (págs. 1-8). New Jersey, USA: Institute of Electrical and Electronics Engineers.
Maass, W. (1997). Networks of spiking neurons: The third generation of neural network.
Matadamas, I. C. (2014). Tesis de maestría : Aplicación de las redes neuronales pulsantes en el reconocimiento de patrones y análisis de imágenes. Mexico D.F.: Centro de Investigación en Computación - Instituto Politécnico Nacional.
McField, D., & Bauer, T. (2011). A review of noninvasive staging of the mediastinum for non-small cell lung carcinoma. Surgery Oncololy Clinical National America, 20:681–90.
Monga, O. (1987). An Optimal Region Growing Algorithm for Image Segmentation. Int. Journal of Pattern Recognition and Art. Intelligence, 351-375.
Papadopoulos, A., Fotiadis, D., & Likas, A. (2005). Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artificial Intelligence in Medicine , 34(2):141–50.
Perez-Carrasco, J. A., Acha, B., Serrano, C., Camunas-Mesa, L., Serrano-Gotarredona, T., & Linares-Barranco, B. (2010). Fast vision through frameless event-based sensing and convolutional processing: Application to texture recognition. Neural Networks IEEE Trans.
Ratnasingam, S., & McGinnity, T. (2011). A spiking neural network for tactile form based object recognition. The 2011 International Joint Conference on Neural Networks (IJCNN). New Jersey, USA: Institute of Electrical and Electronics Engineers-IEEE.
Resch, C., & Szabo, Z. (1994). Analysis of dynamic positron emission tomography images using a neural network.
Rohren, E., Turkington, T., & Coleman, R. (2004). Clinical applications of PET in oncology. Radiology. PubMed, 231(2):305–332.
Roth, H., Lu, L., Seff, A., Cherry, K., Hoffman, J., & Wang, S. (2014). A new 2.5 D representation for lymph node detection using random sets ofdeep convolutional neural network observations. International Conference on Medical Image Computing and Computer-Assisted Intervention (págs. 520-7). Boston, MA, USA. : Springer International Publishing.
Rui, X., Kido, S., Suga, K., Hirano, Y., Tachibana, R., & Muramatsu, K. (2014). Texture analysis on 18F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Japan Circ. Journal., 58:95–9.
Russ, J. (1995). The Image processing Handbook. 2nd edition, CRC Press.
Schmidt-Hansen, M., Baldwin, D., Hasler, E., Zamora, J., Abraira, V., & Roque, I. (2014). PET-CT for assessing mediastinal lymph node involvement in patients with suspected resectable non-small cell lung cancer. Cochrane Database Syst Rev, ;11:CD009519.
Shen, D., Wu, G., & Suk, H.-I. (2017). Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering, 19(1).
Silvestri, G., Gonzalez, A., Jantz, M., Margolis, M., Gould, M., & Tanoue, L. (2013). Methods for staging non-small cell lung cancer: diagnosis and management of lung cancer. American College of Chest Physicians evidence-based clinical practice guidelines, e211s-50s.
Silvestri, G., Gould, M., Margolis, M., Tanoue, L., McCrory, D., & Toloza, E. (2007). Noninvasive staging of non-small cell lung cancer: (2nd edition). ACCP evidenced-based clinical practice guidelines, 132:178S–201S.
Suzuki, J., & Suda, T. (2005). A middleware platform for a biologically inspired network.
Tajbakhhsh N, L. Q. (2016). Stacked sparse autoencoder (SSAE) for nuclei detectionon breast cancer histopathology images. IEEE Trans Med Imag, 119-130.
Tajbakhs, N., Gurudu, S., & Liang, J. (2016). Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Medical Imaging, 35:630–44.
Tajbakhsh, N., Gotway, M., & Liang, J. (2015). Computer-aided pulmonary embolism detection using a novel vesselaligned multi-planar image representation and convolutional neural networks. International Conference on Medical Image Computing and Computer-Assisted Intervention (págs. 62-69). Munich, Germany: Springer International Publishing.
University of Applied Sciences. (2016). Machine Learning Repository. Recuperado el 2019, de http://archive.ics.uci.edu: http://archive.ics.uci.edu/ml/datasets/banknote+authentication
Vaidya, M., Creach, K., Frye, J., Dehdashti, F., Bradley, J., & Naqa, I. (2012). Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiotherapy Oncology Journal European Society Therapeutic Radiology Oncology, 102:239–45.
Verma B, & Zakos J. (2001). A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. Inf Technol Biomed IEEE Trans, 5(1):46-54.
Vincent, L., & Dougherty, E. (1994). Image Analysis and Mathematical Morphology – Vol I. ed. Academic Press.
Wang, J., Welch, K., Wang, L., & Kong, F. (2012). Negative predictive value of positron emission tomography and computed tomography for stage T1-2N0 non-small-cell lung cancer. Clinical Lung Cancer, 13:81–9.
Weiss, L. (1985). Principles of metastasis.
World Health Organization. (2017). Guide To Cancer Early Diagnosis.
World Health Organization. (12 de September de 2018). World Health Organization Site. Recuperado el 2019, de who.int: https://www.who.int/es/news-room/fact-sheets/detail/cancer
Yu, H., Caldwell, C., Mah, K., & Mozeg, D. (2009). Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning. IEEE Trans Med Imag, 28:374–83.
Zheng, Y., Sun, X., Wang, J., Zhang, L., Di, X., & Xu, Y. (2014). FDG-PET/CT imaging for tumor staging and definition of tumor volumes in radiation treatment planning in non-small cell lung cancer. . Oncology Letters, 7:1015–20.