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ORCIDGábor Hullám
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2020 – today
- 2024
[c9]Daniel Hadhazi, Mihaly Vetro
, Gábor Hullám:
Detection of Microcalcifications in Mammograms Using Algorithmic Filtering and Region-Based Neural Networks. MeMeA 2024: 1-6- [c8]Adam Tumay, Daniel Hadhazi, Gábor Hullám:
Heart Segmentation on PA Chest X-Ray Images by Model-Based Deep Learning Approach. MeMeA 2024: 1-6 - 2023
- [c7]Gabor Revy, Daniel Hadhazi, Gábor Hullám:
Automatic Spine Segmentation in CT Scans. BIOIMAGING 2023: 86-93 - 2022
- [c6]Gabor Revy, Daniel Hadhazi, Gábor Hullám:
Automatic lung segmentation in CT scans using guided filtering. ICBRA 2022: 102-110
2010 – 2019
- 2017
- [c5]Gábor Hullám, Gabriella Juhász, Bill Deakin, Peter Antal:
Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: Beyond edge weights in psychological networks. CIBCB 2017: 1-8 - 2016
- [b1]Gábor Hullám:
Bayesian Relevance and Effect Size Measures. Budapest University of Technology and Economics, Hungary, 2016 - 2014
- [c4]Gábor Hullám, Peter Antal:
Towards a Bayesian Decision Theoretic Analysis of Contextual Effect Modifiers. Probabilistic Graphical Models 2014: 222-237 - 2010
- [c3]Gábor Hullám, Peter Antal, Csaba Szalai, András Falus:
Evaluation of a Bayesian model-based approach in GA studies. MLSB 2010: 30-43
2000 – 2009
- 2008
- [c2]Peter Antal, András Millinghoffer, Gábor Hullám, Csaba Szalai, András Falus:
A Bayesian View of Challenges in Feature Selection: Feature Aggregation, Multiple Targets, Redundancy and Interaction. FSDM 2008: 74-89 - 2006
- [c1]Peter Antal, András Gézsi, Gábor Hullám, András Millinghoffer:
Learning Complex Bayesian Network Features for Classification. Probabilistic Graphical Models 2006: 9-16
Coauthor Index
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