A database for publications published by researchers and students at SimulaMet.
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Publication type
- All (359)
- Journal articles (130)
- Books (2)
- Edited books (1)
- Proceedings, refereed (161) Remove Proceedings, refereed <span class="counter">(161)</span> filter
- Book chapters (6)
- Talks, keynote (1)
- PhD theses (5)
- Proceedings, non-refereed (2)
- Posters (7)
- Talks, invited (18)
- Talks, contributed (15)
- Public outreach (3)
- Miscellaneous (8)
Proceedings, refereed
Multimedia datasets: challenges and future possibilities
In International conference on multimedia modeling, 2023.Status: Accepted
Multimedia datasets: challenges and future possibilities
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | International conference on multimedia modeling |
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
In Nordic Artificial Intelligence Research and Development. Springer, 2023.Status: Published
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | Nordic Artificial Intelligence Research and Development |
Pagination | 81-93 |
Publisher | Springer |
Proceedings, refereed
Phenotyping of cervical cancer risk groups via generalized low-rank models using medical questionnaires
In Norwegian AI Symposium. Springer, 2022.Status: Accepted
Phenotyping of cervical cancer risk groups via generalized low-rank models using medical questionnaires
Afilliation | Machine Learning |
Project(s) | DeCipher |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Norwegian AI Symposium |
Publisher | Springer |
Unsupervised Image Segmentation via Self-Supervised Learning Image Classification
In MediaEval 2021. Working Notes Proceedings of the MediaEval 2021 Workshop ed. CEUR Workshop Proceedings, 2022.Status: Published
Unsupervised Image Segmentation via Self-Supervised Learning Image Classification
This paper presents the submission of team Medical-XAI for the Medico: Transparency in Medical Image Segmentation task held at MediaEval 2021. We propose an unsupervised method that utilizes tools from the field of explainable artificial intelligence to create segmentation masks. We extract heat maps, which are useful in order to explain how the `black box' model predicts the category of a certain image, and the segmentation masks are directly derived from the heat maps. Our results show that the created masks can capture the relevant findings to a certain extent using only a small amount of image-level labeled data for the classification model and no segmentation masks at all for the training. This is promising for addressing different challenges within the intersection of artificial intelligence for medicine such as availability of data, cost of labeling and interpretable and explainable results.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | MediaEval 2021 |
Edition | Working Notes Proceedings of the MediaEval 2021 Workshop |
Publisher | CEUR Workshop Proceedings |
Keywords | clustering, Explainable artificial intelligence, Global Features, Grad-CAM, Image segmentation, Medical imaging, Polyp Detection, Self-supervised learning |
URL | http://ceur-ws.org/Vol-3181/ |
Multi-task FMRI Data Fusion using IVA and PARAFAC2
In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022.Status: Published
Multi-task FMRI Data Fusion using IVA and PARAFAC2
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Pagination | 1466-1470 |
Publisher | IEEE |
DOI | 10.1109/ICASSP43922.2022.9747662 |
GMSRF-Net: An Improved generalizability with Global Multi-Scale Residual Fusion Network for Polyp Segmentation
In 26th International Conference on Pattern Recognition. IEEE, 2022.Status: Published
GMSRF-Net: An Improved generalizability with Global Multi-Scale Residual Fusion Network for Polyp Segmentation
Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy. However, polyp segmentation datasets collected from varied centers can follow different imaging protocols leading to difference in data distribution. As a result, most methods suffer from performance drop when trained and tested on different distributions and therefore, require re-training for each specific dataset. We address this generalizability issue by proposing a global multi-scale residual fusion network (GMSRF-Net). Our proposed network maintains high-resolution representations by performing multi-scale fusion operations across all resolution scales through dense connections while preserving low-level information. To further leverage scale information, we design cross multi-scale attention (CMSA) module that uses multi-scale features to identify, keep, and propagate informative features. Additionally, we introduce multi-scale feature selection (MSFS) modules to perform channel-wise attention that gates irrelevant features gathered through global multi-scale fusion within the GMSRF-Net. The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of our network. Experiments conducted on two different polyp segmentation datasets show that our proposed GMSRF-Net outperforms the previous top-performing state-of-the-art method by 8.34% and 10.31% on unseen CVC-ClinicDB and on unseen Kvasir-SEG, in terms of dice coefficient. Additionally, when tested on unseen CVC-ColonDB, we surpass the state-of-the-art method by 9.38% and 4.04% in terms of dice coefficient, when source dataset is Kvasir-SEG and CVC-ClinicDB, respectively.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 26th International Conference on Pattern Recognition |
Publisher | IEEE |
DOI | 10.1109/ICPR56361.2022.9956726 |
Metrics Reloaded - A new recommendation framework for biomedical image analysis validation
In Medical Imaging with Deep Learning. MIDL 2022, 2022.Status: Published
Metrics Reloaded - A new recommendation framework for biomedical image analysis validation
Meaningful performance assessment of biomedical image analysis algorithms depends on objective and appropriate performance metrics. There are major shortcomings in the current state of the art. Yet, so far limited attention has been paid to practical pitfalls associated when using particular metrics for image analysis tasks. Therefore, a number of international initiatives have collaborated to offer researchers with guidance and tools for selecting performance metrics in a problem-aware manner. In our proposed framework, the characteristics of the given biomedical problem are first captured in a problem fingerprint, which identifies properties related to domain interests, the target structure(s), the input datasets, and algorithm output. A problem category-specific mapping is applied in the second step to match fingerprints to metrics that reflect domain requirements. Based on input from experts from more than 60 institutions worldwide, we believe our metric recommendation framework to be useful to the MIDL community and to enhance the quality of biomedical image analysis algorithm validation.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Medical Imaging with Deep Learning |
Publisher | MIDL 2022 |
URL | https://openreview.net/forum?id=24kBqy8rcB_ |
Reprint Edition | https://openreview.net/forum?id=24kBqy8rcB_ |
Prediction Modeling in Activity eCoaching for Tailored Recommendation Generation: A Conceptualization
In International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2022.Status: Published
Prediction Modeling in Activity eCoaching for Tailored Recommendation Generation: A Conceptualization
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | International Symposium on Medical Measurements and Applications (MeMeA) |
Publisher | IEEE |
DOI | 10.1109/MeMeA54994.2022.9856556 |
ICDAR’22: Intelligent Cross-Data Analysis and Retrieval
In 2022 International Conference on Multimedia Retrieval. 2022, Newark, NJ, USA: ACM, 2022.Status: Published
ICDAR’22: Intelligent Cross-Data Analysis and Retrieval
We have witnessed the rise of cross-data against multimodal data
problems recently. The cross-modal retrieval system uses a textual
query to look for images; the air quality index can be predicted
using lifelogging images; the congestion can be predicted using
weather and tweets data; daily exercises and meals can help to
predict the sleeping quality are some examples of this research
direction. Although vast investigations focusing on multimodal data
analytics have been developed, few cross-data (e.g., cross-modal
data, cross-domain, cross-platform) research has been carried on.
In order to promote intelligent cross-data analytics and retrieval
research and to bring a smart, sustainable society to human beings,
the specific article collection on "Intelligent Cross-Data Analysis
and Retrieval" is introduced. This Research Topic welcomes those
who come from diverse research domains and disciplines such as
well-being, disaster prevention and mitigation, mobility, climate
change, tourism, healthcare, and food computing.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 2022 International Conference on Multimedia Retrieval |
Date Published | 06/2022 |
Publisher | ACM |
Place Published | 2022, Newark, NJ, USA |
Neighborhood Graph Neural Networks under Random Perturbations and Quantization Errors
In IEEE International Conference on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2022.Status: Published
Neighborhood Graph Neural Networks under Random Perturbations and Quantization Errors
Afilliation | Machine Learning |
Project(s) | Signal and Information Processing for Intelligent Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | IEEE International Conference on Signal Processing Advances in Wireless Communications (SPAWC) |
Publisher | IEEE |
Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS DEEPCOBOT grant 306640/O70 from the Research Council of Norway. |
DOI | 10.1109/SPAWC51304.2022.9834020 |