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All Outputs (121)

SoftED: Metrics for Soft Evaluation of Time Series Event Detection (2024)
Journal Article
Salles, R., Lima, J., Reis, M., Coutinho, R., Pacitti, E., Masseglia, F., Akbarinia, R., Chen, C., Garibaldi, J., Porto, F., & Ogasawara, E. (in press). SoftED: Metrics for Soft Evaluation of Time Series Event Detection. Computers and Industrial Engineering,

Time series event detectors are evaluated mainly by standard classification metrics, focusing solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring dete... Read More about SoftED: Metrics for Soft Evaluation of Time Series Event Detection.

Explain the world – Using causality to facilitate better rules for fuzzy systems (2024)
Journal Article
Zhang, T., Wagner, C., & Garibaldi, J. M. (2024). Explain the world – Using causality to facilitate better rules for fuzzy systems. IEEE Transactions on Fuzzy Systems, 1-14. https://doi.org/10.1109/tfuzz.2024.3457962

The rules of a rule-based system provide explanations for its behaviour by revealing the relationships between the variables captured. However, ideally, we have AI systems which go beyond explainable AI (XAI), that is, systems which not only explain... Read More about Explain the world – Using causality to facilitate better rules for fuzzy systems.

Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case (2024)
Preprint / Working Paper
Chen, C., Wagner, C., & Garibaldi, J. M. (2024). Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case

Since their introduction, fuzzy sets and systems have become an important area of research known for its versatility in modelling, knowledge representation and reasoning, and increasingly its potential within the context explainable AI. While the app... Read More about Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case.

A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem (2024)
Journal Article
Lin, B., Li, J., Cui, T., Jin, H., Bai, R., Qu, R., & Garibaldi, J. (2024). A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem. Expert Systems with Applications, 249, Article 123515. https://doi.org/10.1016/j.eswa.2024.123515

The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern... Read More about A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem.

Boundary-wise loss for medical image segmentation based on fuzzy rough sets (2024)
Journal Article
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2024). Boundary-wise loss for medical image segmentation based on fuzzy rough sets. Information Sciences, 661, Article 120183. https://doi.org/10.1016/j.ins.2024.120183

The loss function plays an important role in deep learning models as it determines the model convergence behavior and performance. In semantic segmentation, many methods utilize pixel-wise (e.g. cross-entropy) and region-wise (e.g. dice) losses while... Read More about Boundary-wise loss for medical image segmentation based on fuzzy rough sets.

A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty (2022)
Journal Article
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2022). A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty. IEEE Transactions on Fuzzy Systems, 31(8), 2532-2544. https://doi.org/10.1109/tfuzz.2022.3228332

Deep learning methods have achieved an excellent performance in medical image segmentation. However, the practical application of deep learning-based segmentation models is limited in clinical settings due to the lack of reliable information about th... Read More about A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty.

Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease (2022)
Journal Article
Gonem, S., Taylor, A., Figueredo, G., Forster, S., Quinlan, P., Garibaldi, J. M., McKeever, T. M., & Shaw, D. (2022). Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease. Respiratory Research, 23, Article 203. https://doi.org/10.1186/s12931-022-02130-6

Background: The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop... Read More about Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease.

LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint (2022)
Journal Article
Jafari, M., Francis, S., Garibaldi, J. M., & Chen, X. (2022). LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint. Medical Image Analysis, 81, Article 102536. https://doi.org/10.1016/j.media.2022.102536

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the... Read More about LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint.

Lessons learned from the COVID-19 pandemic about sample access for research in the UK (2022)
Journal Article
Mai Sims, J., Lawrence, E., Glazer, K., Gander, A., Fuller, B., Garibaldi, J., …Quinlan, P. R. (2022). Lessons learned from the COVID-19 pandemic about sample access for research in the UK. BMJ Open, 12(4), Article e047309. https://doi.org/10.1136/bmjopen-2020-047309

Objective Annotated clinical samples taken from patients are a foundation of translational medical research and give mechanistic insight into drug trials. Prior research by the Tissue Directory and Coordination Centre (TDCC) indicated that researcher... Read More about Lessons learned from the COVID-19 pandemic about sample access for research in the UK.

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis (2020)
Journal Article
Chernbumroong, S., Johnson, J., Gupta, N., Miller, S., Mccormack, F. X., Garibaldi, J. M., & Johnson, S. R. (2021). Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis. European Respiratory Journal, 57(6), Article 2003036. https://doi.org/10.1183/13993003.03036-2020

Background: Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to ident... Read More about Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis.

A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems (2020)
Journal Article
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2021). A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 29(11), 3323-3333. https://doi.org/10.1109/TFUZZ.2020.3018379

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainabil... Read More about A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems.

End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network (2020)
Journal Article
Xie, R., Liu, J., Cao, R., Qiu, C. S., Duan, J., Garibaldi, J., & Qiu, G. (2020). End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network. IEEE Transactions on Medical Imaging, 40(1), 116-128. https://doi.org/10.1109/TMI.2020.3023254

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to dir... Read More about End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network.

A Comprehensive Study of the Efficiency of Type-Reduction Algorithms (2020)
Journal Article
Chen, C., Wu, D., Garibaldi, J. M., John, R. I., Twycross, J., & Mendel, J. M. (2021). A Comprehensive Study of the Efficiency of Type-Reduction Algorithms. IEEE Transactions on Fuzzy Systems, 29(6), 1556 -1566. https://doi.org/10.1109/tfuzz.2020.2981002

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there have been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karni... Read More about A Comprehensive Study of the Efficiency of Type-Reduction Algorithms.

Constrained Interval Type-2 Fuzzy Sets (2020)
Journal Article
Dalterio, P., Garibaldi, J. M., John, R., & Pourabdollah, A. (2021). Constrained Interval Type-2 Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 29(5), 1212-1225. https://doi.org/10.1109/tfuzz.2020.2970911

In many contexts, type-2 fuzzy sets are obtained from a type-1 fuzzy set to which we wish to add uncertainty. However, in the current type-2 representation there is no restriction on the shape of the footprint of uncertainty and the embedded sets tha... Read More about Constrained Interval Type-2 Fuzzy Sets.

Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach (2020)
Journal Article
Soria, D., Razak, T. R., Garibaldi, J. M., Pourabdollah, A., & Wagner, C. (2021). Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach. IEEE Transactions on Fuzzy Systems, 29(5), 1160-1172. https://doi.org/10.1109/tfuzz.2020.2969901

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as: "How can we measure their interpretability?", "How can we make an informed ass... Read More about Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach.

FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net (2019)
Book Chapter
Jafari, M., Li, R., Xing, Y., Auer, D., Francis, S., Garibaldi, J., & Chen, X. (2019). FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net. In Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23–25, 2019, Proceedings, Part II (529-537). Springer Verlag. https://doi.org/10.1007/978-3-030-34110-7_44

© 2019, Springer Nature Switzerland AG. In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model, known as U-net. U-net is firs... Read More about FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net.

Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification (2019)
Journal Article
Wang, Y., Hu, Q., Zhu, P., Li, L., Lu, B., Garibaldi, J. M., & Li, X. (2020). Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification. IEEE Transactions on Fuzzy Systems, 28(7), 1395-1406. https://doi.org/10.1109/tfuzz.2019.2936801

Deep learning models often use a flat softmax layer to classify samples after feature extraction in visual classification tasks. However, it is hard to make a single decision of finding the true label from massive classes. In this scenario, hierarchi... Read More about Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification.

ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems (2019)
Journal Article
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2020). ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems. IEEE Transactions on Fuzzy Systems, 28(10), 2302-2312. https://doi.org/10.1109/tfuzz.2019.2933787

Non-Singleton Fuzzy Logic Systems (NSFLSs) have the potential to capture and handle input noise within the design of input fuzzy sets. In this paper, we propose an online learning method which utilises a sequence of observations to continuously updat... Read More about ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems.

Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles (2019)
Journal Article
Agrawal, U., Soria, D., Wagner, C., Garibaldi, J., Ellis, I. O., Bartlett, J. M. S., …Green, A. R. (2019). Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles. Artificial Intelligence in Medicine, 97, 27-37. https://doi.org/10.1016/j.artmed.2019.05.002

Breast Cancer is one of the most common causes of cancer death in women, representing a very complex disease with varied molecular alterations. To assist breast cancer prognosis, the classification of patients into biological groups is of great signi... Read More about Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles.