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ORCIDTomás Majtner
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2020 – today
- 2021
[c12]Tomás Majtner
:
HEp-2 Cell Image Recognition with Transferable Cross-Dataset Synthetic Samples. CAIP (1) 2021: 215-225- [c11]Tomás Majtner, Buda Bajic, Jürgen Herp:
Texture-Based Image Transformations for Improved Deep Learning Classification. CIARP 2021: 207-216 - 2020
- [j5]Tomás Majtner, Esmaeil S. Nadimi, Knud Bonnet Yderstraede, Victoria Blanes-Vidal:
Non-invasive detection of diabetic complications via pattern analysis of temporal facial colour variations. Comput. Methods Programs Biomed. 196: 105619 (2020) - [j4]Buda Bajic, Tomás Majtner, Joakim Lindblad, Natasa Sladoje:
Generalised deep learning framework for HEp-2 cell recognition using local binary pattern maps. IET Image Process. 14(6): 1201-1208 (2020)
2010 – 2019
- 2019
- [j3]Amaya Jiménez, Slavica Jonic, Tomás Majtner, Joaquín Otón, Jose Luis Vilas, David Maluenda, Javier Mota, Erney Ramírez-Aportela, Marta Martínez, Yaiza Rancel, Joan Segura, Rubén Sánchez García, Roberto Melero, Laura del Cano, Pablo Conesa, Lars Skjaerven, Roberto Marabini, José María Carazo, Carlos Oscar Sánchez Sorzano:
Validation of electron microscopy initial models via small angle X-ray scattering curves. Bioinform. 35(14): 2427-2433 (2019) - [j2]Tomás Majtner, Sule Yildirim Yayilgan, Jon Yngve Hardeberg:
Optimised deep learning features for improved melanoma detection. Multim. Tools Appl. 78(9): 11883-11903 (2019) - [c10]Tomás Majtner, Esmaeil S. Nadimi:
Comparison of Deep Learning-Based Recognition Techniques for Medical and Biomedical Images. CAIP (1) 2019: 492-504 - [c9]Tomás Majtner, Buda Bajic, Joakim Lindblad, Natasa Sladoje, Victoria Blanes-Vidal, Esmaeil S. Nadimi:
On the Effectiveness of Generative Adversarial Networks as HEp-2 Image Augmentation Tool. SCIA 2019: 439-451 - 2018
- [i1]Tomás Majtner, Buda Bajic, Sule Yildirim, Jon Yngve Hardeberg, Joakim Lindblad, Natasa Sladoje:
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification. CoRR abs/1808.05071 (2018) - 2016
- [c8]Tomás Majtner, Kristina Lidayova, Sule Yildirim Yayilgan, Jon Yngve Hardeberg:
Improving skin lesion segmentation in dermoscopic images by thin artefacts removal methods. EUVIP 2016: 1-6 - [c7]Tomás Majtner, Sule Yildirim Yayilgan, Jon Yngve Hardeberg:
Efficient Melanoma Detection Using Texture-Based RSurf Features. ICIAR 2016: 30-37 - [c6]Tomás Majtner, Sule Yildirim Yayilgan, Jon Yngve Hardeberg:
Combining deep learning and hand-crafted features for skin lesion classification. IPTA 2016: 1-6 - [c5]Roman Stoklasa, Tomás Majtner:
Texture analysis of 3D fluorescence microscopy images using RSurf 3D features. ISBI 2016: 1212-1216 - 2014
- [j1]Roman Stoklasa, Tomás Majtner, David Svoboda:
Efficient k-NN based HEp-2 cells classifier. Pattern Recognit. 47(7): 2409-2418 (2014) - [c4]Tomás Majtner, David Svoboda:
Texture analysis using 3D Gabor features and 3D MPEG-7 Edge Histogram descriptor in fluorescence microscopy. IC3D 2014: 1-7 - [c3]Tomás Majtner, Roman Stoklasa, David Svoboda:
RSURF - The Efficient Texture-Based Descriptor for Fluorescence Microscopy Images of HEP-2 Cells. ICPR 2014: 1194-1199 - [c2]Tomás Majtner, David Svoboda:
Comparison of 3D Texture-Based Image Descriptors in Fluorescence Microscopy. IWCIA 2014: 186-195 - 2012
- [c1]Tomás Majtner, David Svoboda:
Extension of Tamura Texture Features for 3D Fluorescence Microscopy. 3DIMPVT 2012: 301-307
Coauthor Index
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