 | (J16) O. Vaculík, E. Chalupová, K. Grešová, T. Majtner, P. Alexiou. Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes. Biology, Vol. 12, No. 10, October 2023, Article 1276, MDPI. |
 | (J15) E. Chalupová, O. Vaculík, J. Poláček, F. Jozefov, T. Majtner, P. Alexiou. ENNGene: an Easy Neural Network model building tool for Genomics. BMC Genomics, Vol. 23, March 2022, Article 248, BioMed Central. |
 | (J14) T. Majtner, J. B. Brodersen, J. Herp, J. Kjeldsen, M. L. Halling, M. D. Jensen. A Deep Learning Framework for Autonomous Detection and Classification of Crohn’s Disease Lesions in the Small Bowel and Colon with Capsule Endoscopy. Endoscopy International Open, Vol. 9, September 2021, E1361–E1370, Thieme. |
 | (J13) T. Majtner, E. S. Nadimi, K. B. Yderstræde, V. Blanes-Vidal. Non-Invasive Detection of Diabetic Complications via Pattern Analysis of Temporal Facial Colour Variations. Computer Methods and Programs in Biomedicine, Vol. 196, November 2020, Article 105619, Elsevier. |
 | (J12) E. S. Nadimi, T. Majtner, K. B. Yderstræde, V. Blanes-Vidal. Facial Erythema Detects Diabetic Neuropathy Using the Fusion of Machine Learning, Random Matrix Theory and Self Organized Criticality. Scientific Reports, Vol. 10, October 2020, Article 16785, Nature Publishing Group. |
 | (J11) B. Bajić⋆, T. Majtner⋆, J. Lindblad, N. Sladoje. Generalized Deep Learning Framework for HEp-2 Image Cell Recognition. IET Image Proccesing, Vol. 14, No. 6, May 2020, pp. 1201–1208, IET. |
 | (J10) D. Maluenda, T. Majtner, P. Horvath, J. L. Vilas, A. Jiménez-Moreno, J. Mota, E. Ramírez-Aportela, R. Sánchez-García, P. Conesa, L. del Caño, Y. Rancel, Y. Fonseca, M. Martínez, G. Sharov, C. A. García, D. Střelák, R. Melero, R. Marabini, J. M. Carazo, C. O. S. Sorzano. Flexible workflows for on-the-fly electron microscopy single-particle image processing using Scipion. Acta Crystallographica Section D, Vol. 75, No. 10, October 2019, pp. 882–894, IUCr. |
 | (J9) V. Blanes-Vidal, T. Majtner, L. D. Avendaño-Valencia, K. B. Yderstræde, E. S. Nadimi. Invisible Color Variations of Facial Erythema: A Novel Early Marker for Diabetic Complications?. Journal of Diabetes Research, Vol. 2019, September 2019, Article 4583895, Hindawi. |
 | (J8) A. Jiménez, S. Jonić, T. Majtner, J. Otón, J. L. Vilas, D. Maluenda, J. Mota, E. Ramírez-Aportela, M. Martínez, Y. Rancel, J. Segura, R. Sánchez-García, R. Melero, L. del Caño, P. Conesa, L. Skjærven, R. Marabini, J. M. Carazo, C. O. S. Sorzano. Validation of electron microscopy initial models via small angle X-ray scattering curves. Bioinformatics, Vol. 35, No. 14, July 2019, pp. 2427–2433, Oxford University Press. |
 | (J7) T. Majtner, S. Yildirim-Yayilgan, J. Y. Hardeberg. Optimised deep learning features for improved melanoma detection. Multimedia Tools and Applications, Vol. 78, No. 9, May 2019, pp. 11883–11903, Springer. |
 | (J6) C. O. S. Sorzano, A. Jiménez, J. Mota, J. L. Vilas, D. Maluenda, M. Martínez, E. Ramírez-Aportela, T. Majtner, J. Segura, R. Sánchez-García, Y. Rancel, L. del Caño, P. Conesa, R. Melero, S. Jonić, J. Vargas, F. Cazals, Z. Freuberg, J. Krieger, I. Bahar, R. Marabini, J. M. Carazo. Survey of the analysis of continuous conformational variability of biological macromolecules by electron microscopy. Acta Crystallographica Section F, Vol. 75, No. 1, January 2019, pp. 19–32, IUCr. |
 | (J5) J. Gómez-Blanco⋆, J. M. de la Rosa-Trevín⋆, R. Marabini⋆, L. del Caño, A. Jiménez, M. Martínez, R. Melero, T. Majtner, D. Maluenda, J. Mota, Y. Rancel, E. Ramírez, J. L. Vilas, M. Carroni, S. Fleischmann, E. Lindahl, A. W. Ashton, M. Basham, D. K. Clare, K. Savage, C. A. Siebert, G. Sharov, C. O. S. Sorzano, P. Conesa, J. M. Carazo. Using Scipion for stream image processing at Cryo-EM facilities. Journal of Structural Biology, Vol. 204, No. 3, December 2018, pp. 457–463, Elsevier. |
 | (J4) J. L. Vilas, N. Tabassum, J. Mota, D. Maluenda, A. Jiménez-Moreno, T. Majtner., J. M. Carazo, S. T. Acton, C. O. S. Sorzano. Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead. Current Opinion in Structural Biology, Vol. 52, October 2018, pp. 127–145, Elsevier. |
 | (J3) C. O. S. Sorzano, J. Vargas, J. L. Vilas, A. Jiménez-Moreno, J. Mota, T. Majtner, D. Maluenda, M. Martínez, R. Sánchez-García, J. Segura, J. Otón, R. Melero, L. del Caño, P. Conesa, J. Gómez-Blanco, Y. Rancel, R. Marabini, J. M. Carazo. Swarm optimization as a consensus technique for Electron Microscopy Initial Volume. Applied Analysis and Optimization, Vol. 2, No. 2, August 2018, pp. 299–313, Yokohama Publishers. |
 | (J2) C. O. S. Sorzano, E. Fernández-Giménez, V. Peredo-Robinson, J. Vargas, T. Majtner, G. Caffarena, J. Otón, J. L. Vilas, J. M. de la Rosa-Trevín, R. Melero, J. Gómez-Blanco, J. Cuenca, L. del Caño, P. Conesa, R. Marabini, J. M. Carazo. Blind estimation of DED camera gain in Electron Microscopy. Journal of Structural Biology, Vol. 203, No. 2, August 2018, pp. 90–93, Elsevier. |
 | (J1) R. Stoklasa, T. Majtner, D. Svoboda. Efficient k-NN based HEp-2 cells classifier. Pattern Recognition, Vol. 47, No. 7, July 2014, pp. 2409–2418, Elsevier. |
 | (C12) T. Majtner. HEp-2 Cell Image Recognition with Transferable Cross-Dataset Synthetic Samples. 19th International Conference on Computer Analysis of Images and Patterns (CAIP 2021), LNCS 13052, pp. 215–225, Springer. |
 | (C11) T. Majtner, B. Bajić, J. Herp. Texture-Based Image Transformations for Improved Deep Learning Classification. 25th Iberoamerican Congress on Pattern Recognition (CIARP 2021), LNCS 12702, pp. 207–216, Springer. |
 | (C10) T. Majtner, E. S. Nadimi. Comparison of Deep Learning-Based Recognition Techniques for Medical and Biomedical Images. 18th International Conference on Computer Analysis of Images and Patterns (CAIP 2019), LNCS 11678, pp. 492–504, Springer. |
 | (C9) T. Majtner, B. Bajić, J. Lindblad, N. Sladoje, V. Blanes-Vidal, E. S. Nadimi. On the Effectiveness of Generative Adversarial Networks as HEp-2 Image Augmentation Tool. 21st Scandinavian Conference on Image Analysis (SCIA 2019), LNCS 11482, pp. 439–451, Springer. |
 | (C8) T. Majtner, S. Yildirim-Yayilgan, J. Y. Hardeberg. Combining Deep Learning and Hand-Crafted Features for Skin Lesion Classification. 6th International Conference on Image Processing Theory, Tools and Applications (IPTA 2016), pp. 1–6, IEEE. |
| (C7) T. Majtner, K. Lidayová, S. Yildirim-Yayilgan, J. Y. Hardeberg. Improving Skin Lesion Segmentation in Dermoscopic Images by Thin Artefacts Removal Methods. 6th European Workshop on Visual Information Processing (EUVIP 2016), pp. 1–6, IEEE. |
 | (C6) T. Majtner, S. Yildirim-Yayilgan, J. Y. Hardeberg. Efficient Melanoma Detection Using Texture-Based RSurf Features. 13th International Conference on Image Analysis and Recognition (ICIAR 2016), LNCS 9730, pp. 30–37, Springer. |
| (C5) R. Stoklasa⋆, T. Majtner⋆. Texture Analysis of 3D Fluorescence Microscopy Images Using RSurf 3D Features. 13th International Symposium on Biomedical Imaging (ISBI 2016), pp. 1212–1216, IEEE. |
| (C4) T. Majtner, D. Svoboda. Texture Analysis Using 3D Gabor Features and 3D MPEG-7 Edge Histogram Descriptor in Fluorescence Microscopy. 4th International Conference on 3D Imaging (IC3D 2014), pp. 1–7, IEEE. |
| (C3) T. Majtner⋆, R. Stoklasa⋆, D. Svoboda. RSurf – The Efficient Texture-based Descriptor for Fluorescence Microscopy Images of HEp-2 Cells. 22nd International Conference on Pattern Recognition (ICPR 2014), pp. 1194–1199, IEEE. |
 | (C2) T. Majtner, D. Svoboda. Comparison of 3D Texture-based Image Descriptors in Fluorescence Microscopy. 16th International Workshop on Combinatorial Image Analysis (IWCIA 2014), LNCS 8466, pp. 186–195, Springer. |
| (C1) T. Majtner, D. Svoboda. Extension of Tamura Texture Features for 3D Fluorescence Microscopy. 2nd Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization and Transmission (3DimPVT 2012), pp. 301–307, IEEE. |
