Outputs (18)

Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science (2024)
Journal Article
Goddard, T. R., Brookes, K. J., Sharma, R., Moemeni, A., & Rajkumar, A. P. (2024). Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science. Cells, 13(3), Article 223. https://doi.org/10.3390/cells13030223

Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into dise... Read More about Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science.

Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset (2023)
Presentation / Conference Contribution
Wu, Z., Moemeni, A., Caleb-Solly, P., & Castle-Green, S. (2023, June). Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset. Presented at 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia

A large number of deep learning based object detection algorithms have been proposed and applied in a wide range of domains such as security, autonomous driving and robotics. In practical usage, objects being occluded are common, and can result in re... Read More about Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset.

Comparing a Graphical User Interface, Hand Gestures and Controller in Virtual Reality for Robot Teleoperation (2023)
Presentation / Conference Contribution
Chen, J., Moemeni, A., & Caleb-Solly, P. (2023, March). Comparing a Graphical User Interface, Hand Gestures and Controller in Virtual Reality for Robot Teleoperation. Presented at Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, Stockholm, Sweden

Robot teleoperation is being explored in a number of application areas, where combining human adaptive intelligence and high precision of robots can provide access to dangerous or inaccessible places, or augment human dexterity. Using virtual reality... Read More about Comparing a Graphical User Interface, Hand Gestures and Controller in Virtual Reality for Robot Teleoperation.

Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities (2022)
Journal Article
Calderon-Ramirez, S., Yang, S., Elizondo, D., & Moemeni, A. (2022). Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities. Applied Soft Computing, 123, Article 108983. https://doi.org/10.1016/j.asoc.2022.108983

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-... Read More about Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities.

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets (2022)
Journal Article
Calderon-Ramirez, S., Oala, L., Torrentes-Barrena, J., Yang, S., Elizondo, D., Moemeni, A., Colreavy-Donnelly, S., Samek, W., Molina-Cabello, M. A., & Lopez-Rubio, E. (2023). Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets. IEEE Transactions on Artificial Intelligence, 4(2), 282-291. https://doi.org/10.1109/tai.2022.3168804

Semi-supervised deep learning (SSDL) is a popular strategy to leverage unlabelled data for machine learning when labelled data is not readily available. In real-world scenarios, different unlabelled data sources are usually available, with varying de... Read More about Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets.

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica (2022)
Journal Article
Calderon-Ramirez, S., Murillo-Hernandez, D., Rojas-Salazar, K., Elizondo, D., Yang, S., Moemeni, A., & Molina-Cabello, M. (2022). A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica. Medical and Biological Engineering and Computing, 60(4), 1159-1175. https://doi.org/10.1007/s11517-021-02497-6

The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a... Read More about A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica.

Improving Uncertainty Estimations for Mammogram Classification Using Semi-Supervised Learning (2021)
Presentation / Conference Contribution
Calderon-Ramırez, S., Murillo-Hernandez, D., Rojas-Salazar, K., Calvo-Valverde, L.-A., Yang, S., Moemeni, A., Elizondo, D., Lopez-Rubio, E., & Molina-Cabello, M. (2021, July). Improving Uncertainty Estimations for Mammogram Classification Using Semi-Supervised Learning. Presented at International Joint Conference on Neural Networks (IJCNN 2021), Online

Computer aided diagnosis for mammogram images have seen positive results through the usage of deep learning architectures. However, limited sample sizes for the target datasets might prevent the usage of a deep learning model under real world scenari... Read More about Improving Uncertainty Estimations for Mammogram Classification Using Semi-Supervised Learning.

Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images (2021)
Journal Article
Calderon-Ramirez, S., Yang, S., Moemeni, A., Elizondo, D., Colreavy-Donnelly, S., Chavarría-Estrada, L. F., & Molina-Cabello, M. A. (2021). Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images. Applied Soft Computing, 111, Article 107692. https://doi.org/10.1016/j.asoc.2021.107692

A key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification o... Read More about Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images.

Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images (2021)
Journal Article
Calderon-Ramirez, S., Yang, S., Moemeni, A., Colreavy-Donnelly, S., Elizondo, D. A., Oala, L., Rodríguez-Capitán, J., Jiménez-Navarro, M., López-Rubio, E., & Molina-Cabello, M. A. (2021). Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images. IEEE Access, 9, 85442 - 85454. https://doi.org/10.1109/ACCESS.2021.3085418

In this work we implement a COVID-19 infection detection system based on chest Xray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with h... Read More about Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images.

Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images (2021)
Presentation / Conference Contribution
Calderon-Ramirez, S., Giri, R., Moemeni, A., Umaña, M., Elizondo, D., Torrents-Barrena, J., & Molina-Cabello, M. A. (2021, January). Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently availabl... Read More about Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images.