Publications
Journal Publications
[028] G. Africano, Otso Arponen, Irina Rinta-Kiikka, Said Pertuz, Transfer learning for the generalization of artificial intelligence for breast cancer detection: a case-control study, Acta Radiologica 2023. [link]
[027] S. Pertuz, David Ortega, Érika Suarez, William Cancino, Gerson Africano, Irina Rinta-Kiikka, Otso Arponen, Sara Paris, Alfonso Lozano, Saliency of breast lesions in breast cancer detection using artificial intelligence, Scientific Reports 13(20545), 2023. [link]
[026] S. Pertuz, O. Reyes, E. Sancristobal, R. Meier, M. Castro, MOOC-based Flipped Classroom for On-campus Teaching in Undergraduate Engineering Courses, IEEE Transactions on Engineering Education 66(5):468-478, 2023. [pdf][link] Proyect VIE2727
[025] A. Hernandez, D. Miranda, S. Pertuz, An in silico study on the detectability of field cancerization through parenchymal analysis of mammograms, Medical Physics 50(10):6379-6389, 2023. [link] [pdf] Project VIE2816
[024] A. Padilla, O. Arponen, I. Rinta-Kiikka, S. Pertuz, Image Retrieval-based Parenchymal Analysis for Breast Cancer Risk Assessment, Medical Physics 49(2):1055-1064, 2022. DOI:10.1002/mp.15378. [link]
[023] A. Hernández, D. Miranda, S. Pertuz, Algorithms and Methods for Computerized Analysis of Mammography Images in Breast Cancer Risk Assessment, Computer Methods and Programs in Biomedicine, 212(106443), 2021. DOI: 10.1016/j.cmpb.2021.106443 [pdf]
[022] A. Tolonen, T. Pekarinen, A. Sassi, J. Kÿtta, W. Cancino, S. Pertuz, O. Arponen, Methodology, clinical applications, and future directions of body composition analysis of computed tomography (CT) images: A review, European Journal of Radiology, 145(109943), 2021. DOI: 10.1016/j.ejrad.2021.109943. [pdf] Movilidad VIE3903
[021] S. Pertuz, Perception of Engineering Students on Remote Teaching with the Flipped-Classroom Strategy, Revista Ingenierías Universidad de Medellín, 20(39):231-250, 2021. DOI:10.22395/rium.v20n39a13. [pdf] Project VIE2727
[020] I. Salazar, S. Pertuz, F. Martínez, A convolutional oculomotor representation to model parkinsonian fixational patterns from magnified videos. Pattern Analysis and Applications, 2020. DOI:10.1007/s10044-020-00922-4.
[019] I. Salazar, S. Pertuz, F. Martinez, Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps, TecnoLogicas, 23(48):143-161, 2020. [pdf]
[018] E. Dench, D. Bond-Smith, E. Darcey et al., Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk, BMJ Open 9:e031041, 2019. DOI: 10.1136/bmjopen-2019-031041. [pdf]
[017] S. Pertuz, D. A. Miranda, Field Cancerization in the Understanding of Parenchymal Analysis of Mammograms for Breast Cancer Risk Assessment, Medical Hypotheses 136:109511, 2020. DOI:10.1016/j.mehy.2019.109511 [pdf] Project VIE2544.
[016] S. Pertuz, A. Sassi, K. Holi-Hellenius, J. Kamarainen, I. Rinta-Kiikka, A. L. Laaperi, O. Arponen, Clinical Evaluation of a Fully-automated Parenchymal Analysis Software for Breast Cancer Risk Assessment: a Pilot Study in a Finnish Sample, European Journal of Radiology. 121:108710, 2019. DOI: 10.1016/j.ejrad.2019.108710. [pdf] Project VIE2544.
[015] S. Pertuz, A. Sassi, M. Karivaara-Mäkelä, K. Holli-Helenius, A. L. Lääperi, I. Rinta-Kiikka, O. Arponen, J. K. Kämäräinen, Micro-parenchymal patterns for breast cancer risk assessment, Biomedical Physics & Engineering Express. 5(6):065008, 2019. DOI:10.1088/2057-1976/ab42f4. [pdf]
[014] J. Fu, S. Pertuz, J. Matas, J. Kamarainen, Performance analysis of single-query 6-DoF camera pose estimation in self-driving setups, Computer Vision and Image Understanding. 189:58-73, 2019. DOI:10.1016/j.cviu.2019.04.009 [pdf]
[013] S. Pertuz, E. Pulido-Herrera, J. Kamarainen, Focus Model for Metric Depth Estimation in Standard Plenoptic Cameras, ISPRS Journal of Photogrammetry and Remote Sensing. 144:38-47, 2018. DOI:10.1016/j.isprsjprs.2018.06.020 [pdf]
[012] S. Pertuz, J. I. Torres, Lineamientos para el diseño de cursos online masivos abiertos (MOOC) en ingeniería electrónica, Entre Ciencia e Ingeniería, 11(21):42-49, 2017. [pdf]
[011] E. Mojica, S. Pertuz, H. Arguello, High-resolution coded-aperture design for compressive X-ray tomography using low resolution detectors, Optics Communications 404C:103-109, 2017. 10.1016/j.optcom.2017.06.053 [pdf]
[010] S. Pertuz, M. A. Garcia, D. Puig, H. Arguello, Closed-form focus profile model for conventional digital cameras, International Journal of Computer Vision, 124(3):273-286. DOI:10.1007/s11263-017-1024-8 [pdf]
[009] L. Chen, B. Keller, S. Pertuz, S. Ray, D. Kontos, The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006, Radiology, 280(3):693-700 , 2016. DOI:10.1148/radiol.2016151749
[008] S. Pertuz, E. McDonald, S. Weinstein, E. Conant, D. Kontos., Fully-automated quantitative estimation of volumetric breast density from digital breast tomosynthesis images, Radiology, 279(1): 65-74, 2015. DOI:10.1148/radiol.2015150277
[007] C. Nurra, L. Pitol, R. Carraud, S. Pertuz, D. Puig, M. A. Garcia, J. Salvado, C. Torras, Toward the prediction of porous membrane permeability from morphological data, Polymer Engineering & Science, 56(1): 118-124, 2016. DOI:10.1002/pen.24198
[006 ] S. Pertuz, M. A. Garcia, D. Puig, Efficient focus sampling through depth-of-field calibration, International Journal of Computer Vision, 112:342-353, 2015. DOI:10.1007/s11263-014-0770-0 [pdf]
[005] S. Pertuz, M. A. Garcia, D. Puig, Focus-aided, scene segmentation, Computer Vision and Image Understanding, 133:66-75, 2015. DOI:10.1016/j.cviu.2014.09.009 [pdf]
[004] S. Pertuz, M. A. Garcia, D. Puig, Reliability measure for shape-from-focus, Image and Vision Computing, 31(10), pp. 725-734, 2013. DOI:10.1016/j.imavis.2013.07.005 [pdf]
[003] S. Pertuz, D. Puig, M. A. Garcia, A. Fusiello, Generation of all-in-focus images through noise-robust selective fusion of limited depth-of-field images, IEEE Transactions on Image Processing, 22(3), pp. 1242-1251, 2013. DOI:10.1109/TIP.2012.2231087 [pdf]
[002] S. Pertuz, D. Puig, M. A. Garcia, Analysis of focus measure operators in shape-from-focus, Pattern Recognition, 46(5), pp. 1415-1432, 2011. DOI:10.1016/j.patcog.2012.11.011 [pdf]
[001] S. Pertuz, H. R. Ibañez, Sistema de Adquisición Automática de Imagenes para Microscopio Óptico, Ingeniería & Desarrollo, 22, pp. 23-27, 2007. [pdf]
Conference proceedings
[022] W. Cancino, S. Pertuz, Automatic Diagnosis of Autism Using Multilevel Wavelet Decomposition and Support Vector Machine, Latin-american Conference on Biomedical Imaging (CLAIB), 2022. [pdf]
[023] S. Pertuz, O. Reyes, A. Ramírez, Course Quality Assessment in Postpandemic Higher Education, Learning with MOOCS VIII (LWMOOCS), 120-125, 2022. [pdf] Project VIE2727
[020] S. Benítez, O. Arponen, A. Laaperi, I. Rinta-Kiikka, S. Pertuz, Automatic Dense Tissue Segmentation in Digital Mammography Images Based on Fully Convolutional Network and Intensity-Based Clustering, Colombian Conference on Applicacions of Computational Intelligence, Jul. 2022. DOI: 10.1109/ColCACI56938.2022.9905248 [pdf]
[019] A. F. Vargas, A. Hernádez, A. Ramírez, S. Pertuz, On the feasibility of radiomic analysis for the detection of breast lesions on speed-of-sound images of the breast, Medical Image Understanding and Analysis, Jul. 2022. [pdf]
[018] A. F. Vargas and S. Pertuz, “Breast Cancer Risk Assessment using Gabor Filter Banks and Curvelet Transform,” 2021 XXIII Symp. Image, Signal Process. Artif. Vis., pp. 1–5, Sep. 2021. DOI: 10.1109/STSIVA53688.2021.9592015. [pdf]
[017] W. Cancino, G. Africano, and S. Pertuz, “A Benchmark of Preprocessing Strategies for Autism Classification from Resting-State Functional Magnetic Resonance Imaging,” 2021 XXIII Symp. Image, Signal Process. Artif. Vis., pp. 1–5, Sep. 2021. DOI:10.1109/STSIVA53688.2021.9592011 [pdf].
[016] G. Africano et al., A New Benchmark and Method for the Evaluation of Chest Wall Detection in Digital Mammography, IEEE Engineering in Biology and Medicine Conference (EMBC 2020). DOI: 10.1109/EMBC44109.2020.9175960 [pdf] Project VIE2544.
[015] G. Africano et al., A Comparison of Regions of Interest in Parenchymal Analysis for Breast Cancer Risk Assessment, IEEE Engineering in Biology and Medicine Conference (EMBC 2020). DOI: 10.1109/EMBC44109.2020.9176200 [pdf] Project VIE2544.
[014] I. Salazar, S. Pertuz, W. Contreras, F. Carrillo, Parkinsonian Ocular Fixation Patterns from Magnified Videos and CNN Features, Ibero-american Congress on Pattern Recognition (CIARP 2019), pp740-750, 2019. DOI:10.1007/978-3-030-33904-3_70
[013] G. F. Torres, A. Sassi, O. Arponen, K. Holli-Helenius, A. L. Laaperi, I. Rinta-Kiikka, J. Kamarainen, Morphological Area Gradient: System-independent dense tissue segmentation in mammography images, IEEE Engineering in Biology and Medicine Conference (EMBC 2019). DOI: 10.1109/EMBC.2019.8857320 [pdf]
[012] S. Pertuz, A. Sassi, O. Arponen, K. Holli-Helenius, A. L. Laaperi, I. Rinta-Kiikka, Do Mammographic Systems Affect the Performance of Computerized Parenchymal Analysis? IEEE Engineering in Biology and Medicine Conference (EMBC 2019). DOI:10.1109/EMBC.2019.8856948 [pdf]
[011] S. Pertuz, G. F. Torres, R. Tamimi, J. Kamarainen, Open Framework for Mammography-based Breast Cancer Risk Assessment, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2019). DOI: 10.1109/BHI.2019.8834599 [pdf]
[010] O. Araque, M. P. Mejia-Sandoval, A. Sassi, K. Holli-Hellenius, A. L. Laaperi, I. Rinta-Kiikka, O. Arponen, S. Pertuz, Selecting the Mammographic-View for the Parenchymal Analysis-Based Breast Cancer Risk Assessment, IEEE-EMBS International Conference on Biomedical and Health Informatics, (BHI 2019). DOI:10.1109/BHI.2019.8834461 [pdf]
[009] Y. Qian, S. Pertuz, J. Nikkanen, J.K. Kamarainen, J. Matas, Rivisiting Gray Pixel for Statistical Illumination Estimation, Int. Conf. Computer Vision Theory and Applications (VISSAP 2019), pp. 36-46 . DOI:10.5220/0007406900360046 [pdf]
[008] A. Ainasoja, S. Pertuz, J. K. Kamarainen, J. Matas, Smartphone Teleoperation for Self-Balancing Telepresence Robots, Int. Conf. Computer Vision Theory and Applications (VISSAP 2019), pp 561-568. DOI: 10.5220/0007406405610568. [pdf]
[007] S. Pertuz, J. Kamarainen, Region-based depth recovery for highly sparse depth maps, IEEE International Conference on Image Processing (ICIP 2017), pp. 2074-2078. DOI: 10.1109/ICIP.2017.8296647 [pdf]
[006] M. Marquez, N. Diaz, J. Bacca, S. Pertuz, H. Arguello, Compressive light field spectral imaging in a single-sensor device by using coded apertures, proc. OSA Imaging and Applied Optics 2017. DOI:10.1364/COSI.2017.CTh1B.5
[005] G. F. Torres, S. Pertuz, Automatic Detection of the Retroareolar Region in Mammograms, Proc. Latin American Congress on Biomedical Engineering (CLAIB 2016), pp. 157-160. DOI:10.1007/978-981-10-4086-3_40 [pdf]
[004] S. Pertuz, J. I. Torres, The impact of MOOCs on the Performance of Undergraduate Students in Digital Signal Processing, Proc. Symposium on Signal Processing, Images and Artificial Vision (STSIVA 2016), pp. 1-7. DOI:10.1109/STSIVA.2016.7743356. [pdf]
[003] J. Santamaria, M. A. Marquez, S. Pertuz, A method for improving depth estimation in light field images, Proc. Symposium on Signal Processing, Images and Artificial Vision (STSIVA 2016), pp. 1-7. DOI:10.1109/STSIVA.2016.7743342
[002] S. Pertuz, C. Julia, D. Puig, A novel mammography image representation framework with application to image registration, Proc. International Conference on Pattern Recognition (ICPR 2014), pp. 3292-3297. DOI:10.1109/ICPR.2014.567 [pdf]
[001] S. Pertuz, D. Puig, M. A. Garcia, Improving Shape-from-focus by compensating for image magnification shift, International Conference on Pattern Recognition (ICPR 2010), pp. 802-805. Istanbul, Turkey, August 2010. DOI:10.1109/ICPR.2010.202 [pdf]