No profile picture available

Dr Adrian Galdran

  • Senior Lecturer In Data Science For Medical Imaging And Visualisation Fixed Term
Back to top

Journal Articles

  • 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.
  • Li, J. and Galdran, A., 2020. Multi-focus microscopic image fusion algorithm based on sparse representation and pulse coupled neural network. Acta Microscopica, 29 (4), 1816-1823.
  • 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.
  • 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.
  • 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.
  • 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., 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.
  • Galdran, A., Vazquez-Corral, J., Pardo, D. and Bertalmio, M., 2017. Fusion-based variational image dehazing. IEEE Signal Processing Letters, 24 (2), 151-155.
  • 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.
  • Galdran, A., Dolz, J., Chakor, H., Lombaert, H. and Ayed, I.B.. Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images.

Conferences

  • Galdran, A. and Bouchachia, H., 2020. Residual networks for pulmonary nodule segmentation and texture characterization. 396-405.
  • 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.
  • 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.
  • Galdran, A., Costa, P. and Campilho, A., 2019. Real-time informative laryngoscopic frame classification with pre-trained convolutional neural networks. 87-90.
  • Galdran, A., Meyer, M., Costa, P., Mendonca and Campilho, A., 2019. Uncertainty-aware artery/vein classification on retinal images. 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. 1202-1206.
  • Smailagic, A., Galdran, A. et al., 2019. MedAL: Accurate and Robust Deep Active Learning for Medical Image Analysis. 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. 362-368.
  • Ferreira, F.T., Sousa, P., Galdran, A., Sousa, M.R. and Campilho, A., 2018. End-to-End Supervised Lung Lobe Segmentation.
  • Galdran, A., Costa, P., Vazquez-Corral, J. and Campilho, A., 2018. Weakly Supervised Fog Detection. 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.
  • 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. 82-90.
  • Meyer, M.I., Galdran, A., Costa, P., Mendonça, A.M. and Campilho, A., 2018. Deep Convolutional Artery/Vein Classification of Retinal Vessels. 622-630.
  • 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. 39-47.
  • Galdran, A., Alvarez-Gila, A., Bria, A., Vazquez-Corral, J. and Bertalmío, M., 2018. On the Duality Between Retinex and Image Dehazing. IEEE Computer Society, 8212-8221.
  • Arad, B., Galdran, A. et al., 2018. NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images. 1042-1051.
  • Ancuti, C., Galdran, A. et al., 2018. NTIRE 2018 Challenge on Image Dehazing: Methods and Results. 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. 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. 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. 1-7.
  • Al-Rawi, M., Galdran, A., Elmgren, F., Rodriguez, J., Bastos, J. and Pinto, M., 2017. Landmark detection from sidescan sonar images. 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. 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.
  • 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. 507-515.
  • Costa, P., Galdran, A., Meyer, M.I., Mendonça, A.M. and Campilho, A., 2017. Adversarial synthesis of retinal images from vessel trees. 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. 323-330.
  • 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. 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.
  • 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.
  • Galdran, A., Vazquez-Corral, J., Pardo, D. and Bertalmío, M., 2015. A variational framework for single image Dehazing. 259-270.
  • Galdran, A., Picón, A., Garrote, E. and Pardo, D., 2015. Pectoral muscle segmentation in mammograms based on cartoon-texture decomposition. 587-594.
The data on this page was last updated at 04:17 on October 21, 2020.