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Dr Adrian Galdran

  • Senior Lecturer In Data Science For Medical Imaging And Visualisation Fixed Term
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Journal Articles

  • Pedrosa, J., Galdran, A., Bouchachia, H. et al., 2021. LNDb challenge on automatic lung cancer patient management. Medical Image Analysis, 70.
  • Smailagic, A., Galdran, A. et al., 2020. O-MedAL: Online active deep learning for medical image analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10 (4).
  • Shahriari, M., Pardo, D., Picon, A., Galdran, A., Del Ser, J. and Torres-Verdín, C., 2020. A deep learning approach to the inversion of borehole resistivity measurements. Computational Geosciences, 24 (3), 971-994.
  • Vazquez-Corral, J., Galdran, A., Cyriac, P. and Bertalmío, M., 2020. A fast image dehazing method that does not introduce color artifacts. Journal of Real-Time Image Processing, 17 (3), 607-622.
  • Smailagic, A., Galdran, A. et al., 2020. O-MedAL: Online active deep learning for medical image analysis. Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 10.
  • Galdran, A., Chelbi, J., Kobi, R., Dolz, J., Lombaert, H., Ayed, I.B. and Chakor, H., 2020. Non-uniform label smoothing for diabetic retinopathy grading from retinal fundus images with deep neural networks. Translational Vision Science and Technology, 9 (2 Special Issue), 1-8.
  • Galdran, A., Anjos, A., Dolz, J., Chakor, H., Lombaert, H. and Ayed, I.B., 2020. The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models. CoRR, abs/2009.01907.
  • Galdran, A., Dolz, J., Chakor, H., Lombaert, H. and Ben Ayed, I., 2020. Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12265 LNCS, 665-674.
  • Alvarez-Gila, A., Galdran, A., Garrote, E. and van de Weijer, J., 2019. Self-supervised blur detection from synthetically blurred scenes. Image and Vision Computing, 92.
  • Al Hajj, H., Galdran, A. et al., 2019. CATARACTS: Challenge on automatic tool annotation for cataRACT surgery. Medical Image Analysis, 52, 24-41.
  • Alvarez-Gila, A., Galdran, A., Garrote, E. and Weijer, J.V.D., 2019. Self-supervised blur detection from synthetically blurred scenes. CoRR, abs/1908.10638.
  • Galdran, A., Bria, A., Alvarez-Gila, A., Vazquez-Corral, J. and Bertalmio, M., 2018. On the Duality between Retinex and Image Dehazing. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8212-8221.
  • Galdran, A., 2018. Image dehazing by artificial multiple-exposure image fusion. Signal Processing, 149, 135-147.
  • Costa, P., Galdran, A., Smailagic, A. and Campilho, A., 2018. A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images. IEEE Access, 6, 18747-18758.
  • Costa, P., Galdran, A., Meyer, M.I., Niemeijer, M., Abràmoff, M., Mendonça, A.M. and Campilho, A., 2018. End-to-End Adversarial Retinal Image Synthesis. IEEE Transactions on Medical Imaging, 37 (3), 781-791.
  • Smailagic, A., Noh, H.Y., Costa, P., Walawalkar, D., Khandelwal, K., Mirshekari, M., Fagert, J., Galdran, A. and Xu, S., 2018. MedAL: Deep Active Learning Sampling Method for Medical Image Analysis. CoRR, abs/1809.09287.
  • Araújo, T., Aresta, G., Galdran, A., Costa, P., Mendonça, A.M. and Campilho, A., 2018. Uolo - Automatic object detection and segmentation in biomedical images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11045 LNCS, 165-173.
  • Galdran, A., Araújo, T., Mendonça, A.M. and Campilho, A., 2018. Retinal image quality assessment by mean-subtracted contrast-normalized coefficients. Lecture Notes in Computational Vision and Biomechanics, 27, 844-853.
  • Araújo, T., Aresta, G., Galdran, A., Costa, P., Mendonça, A.M. and Campilho, A., 2018. UOLO - automatic object detection and segmentation in biomedical images. CoRR, abs/1810.05729.
  • Galdran, A., Vazquez-Corral, J., Pardo, D. and Bertalmio, M., 2017. Fusion-based variational image dehazing. IEEE Signal Processing Letters, 24 (2), 151-155.
  • Costa, P., Galdran, A., Meyer, M.I., Abràmoff, M.D., Niemeijer, M., Mendonça, A.M. and Campilho, A., 2017. Towards Adversarial Retinal Image Synthesis. CoRR, abs/1701.08974.
  • Galdran, A., Alvarez-Gila, A., Meyer, M.I., Saratxaga, C.L., Araujo, T., Garrote, E., Aresta, G., Costa, P., Mendonça, A.M. and Campilho, A.J.C., 2017. Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis. CoRR, abs/1703.03702.
  • Bereciartua, A., Picon, A., Galdran, A. and Iriondo, P., 2016. 3D active surfaces for liver segmentation in multisequence MRI images. Computer Methods and Programs in Biomedicine, 132, 149-160.
  • Galdran, A., Vazquez-Corral, J., Pardo, D. and Bertalmío, M., 2015. Enhanced variational image dehazing. SIAM Journal on Imaging Sciences, 8 (3), 1519-1546.
  • Bereciartua, A., Picon, A., Galdran, A. and Iriondo, P., 2015. Automatic 3D model-based method for liver segmentation in MRI based on active contours and total variation minimization. Biomedical Signal Processing and Control, 20, 71-77.
  • Galdran, A., Pardo, D., Picón, A. and Alvarez-Gila, A., 2015. Automatic Red-Channel underwater image restoration. Journal of Visual Communication and Image Representation, 26, 132-145.

Conferences

  • Galdran, A., Carneiro, G. and Ballester, M.A.G., 2021. Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12661 LNCS, 293-307.
  • Galdran, A., Carneiro, G. and Ballester, M.A.G., 2021. A Hierarchical Multi-task Approach to Gastrointestinal Image Analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12668 LNCS, 275-282.
  • Galdran, A., Carneiro, G. and González Ballester, M.A., 2021. Multi-center polyp segmentation with double encoder-decoder networks. CEUR Workshop Proceedings, 2886, 9-16.
  • Galdran, A. and Bouchachia, H., 2020. Residual networks for pulmonary nodule segmentation and texture characterization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12132 LNCS, 396-405.
  • Galdran, A., Dolz, J., Chakor, H., Lombaert, H. and Ayed, I.B., 2020. Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images. MICCAI (5), 12265, 665-674 Springer.
  • Galdran, A., Chakor, H., Abdulaziz, A., Kobbi, R., Christodoulidis, A., Chelbi, J., Racine, M.-A. and Benayed, I., 2019. Automatic classification and triage of diabetic retinopathy from retinal images based on a convolutional neural networks (CNN) method. ACTA OPHTHALMOLOGICA, 97.
  • Costa, P., Araujo, T., Aresta, G., Galdran, A., Mendonca, A.M., Smailagic, A. and Campilho, A., 2019. EyeWeS: Weakly supervised pre-trained convolutional neural networks for diabetic retinopathy detection. Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019.
  • Galdran, A., Costa, P. and Campilho, A., 2019. Real-time informative laryngoscopic frame classification with pre-trained convolutional neural networks. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 87-90.
  • Galdran, A., Meyer, M., Costa, P., Mendonca and Campilho, A., 2019. Uncertainty-aware artery/vein classification on retinal images. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 556-560.
  • Sousa, P., Galdran, A., Costa, P. and Campilho, A., 2019. Learning to segment the lung volume from ct scans based on semi-automatic ground-truth. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 1202-1206.
  • Smailagic, A., Galdran, A. et al., 2019. MedAL: Accurate and Robust Deep Active Learning for Medical Image Analysis. Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, 481-488.
  • Smailagic, A., Sharan, A., Costa, P., Galdran, A., Gaudio, A. and Campilho, A., 2019. Learned pre-processing for automatic diabetic retinopathy detection on eye fundus images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11663 LNCS, 362-368.
  • Ferreira, F.T., Sousa, P., Galdran, A., Sousa, M.R. and Campilho, A., 2018. End-to-End Supervised Lung Lobe Segmentation. Proceedings of the International Joint Conference on Neural Networks, 2018-July.
  • Galdran, A., Costa, P., Vazquez-Corral, J. and Campilho, A., 2018. Weakly Supervised Fog Detection. Proceedings - International Conference on Image Processing, ICIP, 2875-2879.
  • Al-Rawi, M., Sebastien, T., Isasi, A., Galdran, A., Rodriguez, J., Elmgren, F., Bastos, J. and Pinto, M., 2018. A novel algorithm for quasi real-time matching of bathymetric data. Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 7A.
  • Meyer, M.I., Galdran, A., Mendonça, A.M. and Campilho, A., 2018. A pixel-wise distance regression approach for joint retinal optical disc and fovea detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11071 LNCS, 39-47.
  • Galdran, A., Costa, P., Bria, A., Araújo, T., Mendonça, A.M. and Campilho, A., 2018. A no-reference quality metric for retinal vessel tree segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11070 LNCS, 82-90.
  • Meyer, M.I., Galdran, A., Costa, P., Mendonça, A.M. and Campilho, A., 2018. Deep Convolutional Artery/Vein Classification of Retinal Vessels. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10882 LNCS, 622-630.
  • Galdran, A., Alvarez-Gila, A., Bria, A., Vazquez-Corral, J. and Bertalmío, M., 2018. On the Duality Between Retinex and Image Dehazing. CVPR, 8212-8221 IEEE Computer Society.
  • Arad, B., Galdran, A. et al., 2018. NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images. PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 1042-1051.
  • Ancuti, C., Galdran, A. et al., 2018. NTIRE 2018 Challenge on Image Dehazing: Methods and Results. PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 1004-1014.
  • Savelli, B., Bria, A., Galdran, A., Marrocco, C., Molinara, M., Campilho, A. and Tortorella, F., 2017. Illumination Correction by Dehazing for Retinal Vessel Segmentation. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2017-June, 219-224.
  • Galdran, A., Isasi, A., Al-Rawi, M., Rodriguez, J., Bastos, J., Elmgren, F. and Pinto, M., 2017. An efficient non-uniformity correction technique for side-scan sonar imagery. OCEANS 2017 - Aberdeen, 2017-October, 1-6.
  • Al-Rawi, M., Galdran, A., Isasi, A., Elmgren, F., Carbonara, G., Falotico, E., Real-Arce, D.A., Rodriguez, J., Bastos, J. and Pinto, M., 2017. Cubic spline regression based enhancement of side-scan sonar imagery. OCEANS 2017 - Aberdeen, 2017-October, 1-7.
  • Al-Rawi, M., Galdran, A., Elmgren, F., Rodriguez, J., Bastos, J. and Pinto, M., 2017. Landmark detection from sidescan sonar images. 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2017, 2018-January, 1-6.
  • Isasi-Andrieu, A., Garrote-Contreras, E., Iriondo-Bengoa, P., Aldama-Gant, D. and Galdran, A., 2017. Deflectometry setup definition for automatic chrome surface inspection. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 1-4.
  • Al-Rawi, M.S., Galdrán, A., Yuan, X., Eckert, M., Martínez, J.F., Elmgren, F., Cürüklü, B., Rodriguez, J., Bastos, J. and Pinto, M., 2017. Intensity normalization of sidescan sonar imagery. 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016.
  • Costa, P., Galdran, A., Meyer, M.I., Mendonça, A.M. and Campilho, A., 2017. Adversarial synthesis of retinal images from vessel trees. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10317 LNCS, 516-523.
  • Costa, P., Campilho, A., Hooi, B., Smailagic, A., Kitani, K., Liu, S., Faloutsos, C. and Galdran, A., 2017. EyeQual: Accurate, explainable, retinal image quality assessment. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, 2017-December, 323-330.
  • Meyer, M.I., Costa, P., Galdran, A., Mendonça, A.M. and Campilho, A., 2017. A deep neural network for vessel segmentation of Scanning Laser Ophthalmoscopy images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10317 LNCS, 507-515.
  • Bria, A., Marrocco, C., Galdran, A., Campilho, A., Marchesi, A., Mordang, J.J., Karssemeijer, N., Molinara, M. and Tortorella, F., 2017. Spatial Enhancement by Dehazing for Detection of Microcalcifications with Convolutional Nets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10485 LNCS, 288-298.
  • Vazquez-Corral, J., Zamir, S.W., Galdran, A., Pardo, D. and Bertalmío, M., 2016. Image processing applications through a variational perceptually-based color correction related to Retinex. IS and T International Symposium on Electronic Imaging Science and Technology.
  • Vazquez-Corral, J., Zamir, S.W., Galdran, A., Pardo, D. and Bertalmío, M., 2016. Image processing applications through a variational perceptually-based color correction related to Retinex. IS and T International Symposium on Electronic Imaging Science and Technology.
  • Galdran, A., Vazquez-Corral, J., Pardo, D. and Bertalmío, M., 2015. A variational framework for single image Dehazing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8927, 259-270.
  • Galdran, A., Picón, A., Garrote, E. and Pardo, D., 2015. Pectoral muscle segmentation in mammograms based on cartoon-texture decomposition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9117, 587-594.