Alba Ordoñez

Unsupervised Domain Adaptation for Breast Cancer Detection in a Multi-Scanner Environment: A Case-Study from Norway Lecture Notes in Computer Science (LNCS), vol. 15734, pp. 355 364 , (ISSN 0302-9743 1611-3349 ), doi: https://doi.org/10.1007/978-3-031-95838-0_35 , 2025. Vitenskapelig artikkel

Explaining Mammography Models using Mammo-CLIP Dissect 2025. Rapport

Training an AI-model on all seismic data in DISKOS: The seismic foundation model for NCS 2025. Vitenskapelig foredrag

Understanding Chain-of-Thought (CoT) Reasoning in Vision-Language Models for Earth Observation (EO) 2025. Rapport

Leveraging Data with Strong, Weak and No Labels in Marine Acoustics 2025. Poster

Deep learning for marine acoustics: Leveraging data with strong, weak and no labels 2025. Faglig foredrag

Deep learning methods for acoustic target classification 2025. Annen presentasjon

Unsupervised domain adaptation for breast cancer detection in a multi-scanner environment: A case-study from Norway , 2025. Vitenskapelig foredrag

A seismic foundation model for the Norwegian Continental Shelf , 2025. Vitenskapelig foredrag

Interactive Injectite Mapping with Minimal Training Data using Self-Supervised Learning , 2025. Vitenskapelig foredrag

Developing a foundation model for seismic data 2024. Poster

Rotational invariance exploration for seismic CBIR 2024. Rapport

Exploring Concept-Based Explainability in Breast Cancer Classification 2024. Rapport

Explaining a Mammogram Classifier using Concept Relevance Propagation (CRP) and Relevance Maximization (RelMax) 2024. Faglig foredrag

Training a seismic foundation model and using it for interactive labelling 2024. Vitenskapelig foredrag

Foundation Models for Earth Observation 2024. Vitenskapelig foredrag

EO Foundation Model. Foundation Models for Climate and Society. Deliverable D1.1 Milestone 1 2024. Rapport

Assessing present and future risk of water damage using building attributes, meteorology, and topography Journal of the Royal Statistical Society, Series C (Applied Statistics), vol. 72, pp. 809 828 , (ISSN 0035-9254 1467-9876 ), doi: https://doi.org/10.1093/jrsssc/qlad043 , 2023. Vitenskapelig artikkel

Uncertainty in deep neural networks 2023. Vitenskapelig foredrag

Introduction of workshop on interpretability and reliability 2023. Vitenskapelig foredrag

Update on seismic content-based image retrieval 2023. Vitenskapelig foredrag

Searching for similar seismic structures using deep neural networks 2023. Rapport

Searching for Similar Seismic Structures Using Transformer-based Masked Autoencoders 2023. Poster

Improving model understanding of cancer lesions via a challenging dataset 2023. Rapport

DeepOtolith v1.0: An Open-Source AI Platform for Automating Fish Age Reading from Otolith or Scale Images Fishes, vol. 7, (ISSN 2410-3888 ), doi: https://doi.org/10.3390/fishes7030121 , 2022. Vitenskapelig artikkel

Explainability and uncertainty for classification of mammograms 2022. Poster

Learning motion of seismic structures without human labelling 2022. Rapport

Learning motion of seismic structures without human labelling 2022. Rapport

Automatic Fish Age Determination across Different Otolith Image Labs Using Domain Adaptation Fishes, vol. 7, pp. 1 16 , (ISSN 2410-3888 ), doi: https://doi.org/10.3390/fishes7020071 , 2022. Vitenskapelig artikkel

Making sense of AI models 2022. Faglig foredrag

Improving marine acoustic target classification with context information 2022. Poster

Improving marine acoustic target classification with spatial information 2022. Vitenskapelig foredrag

Providing interpretable solutions 2022. Vitenskapelig foredrag

Seismic analogy retrieval: preliminary study 2022. Vitenskapelig foredrag

Seismic analogy retrieval: preliminary study 2022. Poster

Developing and deploying machine learning methods for acoustic data 2022. Vitenskapelig foredrag

Evaluating Different Strategies for Domain Generalization in Mammogram Classifiers 2022. Rapport

An online otolith age reader using deep neural networks: Perspectives and challenges 2022. Vitenskapelig foredrag

Evaluation of echosounder data preparation strategies for modern machine learning models Fisheries Research, vol. 254, (ISSN 0165-7836 1872-6763 ), doi: https://doi.org/10.1016/j.fishres.2022.106411 , 2022. Vitenskapelig artikkel

Machine learning for image-based interpretation of non-verbal communication 2021. Rapport

Deep Domain Adaptation Applied to Fish Age Prediction 2021. Faglig foredrag

Explainability and uncertainty for classification of mammography images 2021. Rapport

Improving marine acoustic target classification with context information 2021. Poster

Using model averaging ensembles in semantic segmentation of marine echosounder data for acoustic classification of species 2021. Vitenskapelig foredrag

Deep domain adaptation applied to automatic fish age prediction 2021. Vitenskapelig foredrag

Learning motion of seismic structures without human labelling 2021. Vitenskapelig foredrag

Fisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acoustics , 2021. Rapport

Machine learning for image-based interpretation of non-verbal communication - Initial version 2021. Rapport

Feasibility study: Non-verbal communication interpreter - Image-based methodological approaches 2020. Rapport

Understanding a Neural Network by Visualization: Application to Fish Age Prediction 2020. Poster

Making sense of deep CNNs for image recognition applications 2020. Vitenskapelig foredrag

Stedsbasert risiko for vannskader - fase 1: Vurdering av topografiske indekser 2020. Rapport

Explaining decisions of deep neural networks used for fish age prediction PLOS ONE, vol. 15, (ISSN 1932-6203 ), doi: https://doi.org/10.1371/journal.pone.0235013 , 2020. Vitenskapelig artikkel

Visualization of deep neural networks applied to fish age prediction 2019. Rapport