Rob Brennan

Teerath Kumar, Alessandra Mileo, Rob Brennan et al.

Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation. Among these, data augmentation, which aims to enlarge the dataset size by including sample diversity, has been a hot topic in recent times. In this article, we focus on advanced data augmentation techniques. we provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation. For results reproducibility, we compiled available codes of all data augmentation techniques. Finally, we discuss the challenges and difficulties, and possible future direction for the research community. We believe, this survey provides several benefits i) readers will understand the data augmentation working mechanism to fix overfitting problems ii) results will save the searching time of the researcher for comparison purposes. iii) Codes of the mentioned data augmentation techniques are available at https://github.com/kmr2017/Advanced-Data-augmentation-codes iv) Future work will spark interest in research community.

Sidra Aleem, Teerath Kumar, Suzanne Little et al.

Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learning algorithms is highly dependent on data quality. Particularly for the medical domain, it is an important aspect as medical data is very sensitive to quality and poor quality can lead to misdiagnosis. To improve the diagnostic performance, research has been done both in complex DL architectures and in improving data quality using dataset dependent static hyperparameters. However, the performance is still constrained due to data quality and overfitting of hyperparameters to a specific dataset. To overcome these issues, this paper proposes random data augmentation based enhancement. The main objective is to develop a generalized, data-independent and computationally efficient enhancement approach to improve medical data quality for DL. The quality is enhanced by improving the brightness and contrast of images. In contrast to the existing methods, our method generates enhancement hyperparameters randomly within a defined range, which makes it robust and prevents overfitting to a specific dataset. To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks. For grayscale imagery, experiments have been performed with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets. Experimental results demonstrate that with the proposed enhancement methodology, DL architectures outperform other existing methods. Our code is publicly available at: https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement

Minh-Khoi Pham, Thang-Long Nguyen Ho, Thao Thi Phuong Dao et al.

Clinical prediction from structured electronic health records (EHRs) is challenging due to high dimensionality, heterogeneity, class imbalance, and distribution shift. While tabular in-context learning (TICL) and retrieval-augmented methods perform well on generic benchmarks, their behavior in clinical settings remains unclear. We present a multi-cohort EHR benchmark comparing classical, deep tabular, and TICL models across varying data scale, feature dimensionality, outcome rarity, and cross-cohort generalization. PFN-based TICL models are sample-efficient in low-data regimes but degrade under naive distance-based retrieval as heterogeneity and imbalance increase. We propose AWARE, a task-aligned retrieval framework using supervised embedding learning and lightweight adapters. AWARE improves AUPRC by up to 12.2% under extreme imbalance, with gains increasing with data complexity. Our results identify retrieval quality and retrieval-inference alignment as key bottlenecks for deploying tabular in-context learning in clinical prediction.

Malick Ebiele, Malika Bendechache, Rob Brennan

Background: Since 1990 many feature selection methods have been proposed across heterogeneous applications. To validate the usefulness of a new method, it needs to be compared against at least one baseline method from the existing literature on a feature selection task using at least one dataset. Recent developments in tabular Deep Learning (DL) and data valuation in Machine Learning (ML) suggest that the evaluation of new methods, algorithms, and models may be consciously or unconsciously biased. We hypothesise that a similar trend exists in feature selection (FS), particularly in filter feature selection (FFS). The aim of this study is therefore to examine FFS studies to identify factors that influence the evaluation and that might consist entry point for biases in order to recommend stronger principles for FFS evaluation. Methods: We analyse a sample of 28 high profile FFS studies published between 1994 and 2025. The analysis provides reflections on how to examine FFS studies, highlights lessons learned throughout the process, and gives five evidence-based recommendations for future FFS evaluation. Results: Multivariate Linear Regression analysis achieved a score of $R^2=0.33$. It means that 33% of the variance in the performance of new methods against chosen baselines (win rate) is explained by the number of datasets (#Datasets), the number of baselines (#Baselines), and the number of new methods (#NewMethods). Discussion: $R^2=0.33$ is considered medium explanation; which is promising given that this is the first such study. The medium explanation result is due to the fact that win rate is influenced by additional factors such as the maturity of the feature selection domain, the type of datasets and baselines, and the simplicity of the regression model used to explain the relationship.

Minh-Khoi Pham, Tai Tan Mai, Martin Crane et al.

Carbapenemase-Producing Enterobacteriace poses a critical concern for infection prevention and control in hospitals. However, predictive modeling of previously highlighted CPE-associated risks such as readmission, mortality, and extended length of stay (LOS) remains underexplored, particularly with modern deep learning approaches. This study introduces an eXplainable AI modeling framework to investigate CPE impact on patient outcomes from Electronic Medical Records data of an Irish hospital. We analyzed an inpatient dataset from an Irish acute hospital, incorporating diagnostic codes, ward transitions, patient demographics, infection-related variables and contact network features. Several Transformer-based architectures were benchmarked alongside traditional machine learning models. Clinical outcomes were predicted, and XAI techniques were applied to interpret model decisions. Our framework successfully demonstrated the utility of Transformer-based models, with TabTransformer consistently outperforming baselines across multiple clinical prediction tasks, especially for CPE acquisition (AUROC and sensitivity). We found infection-related features, including historical hospital exposure, admission context, and network centrality measures, to be highly influential in predicting patient outcomes and CPE acquisition risk. Explainability analyses revealed that features like "Area of Residence", "Admission Ward" and prior admissions are key risk factors. Network variables like "Ward PageRank" also ranked highly, reflecting the potential value of structural exposure information. This study presents a robust and explainable AI framework for analyzing complex EMR data to identify key risk factors and predict CPE-related outcomes. Our findings underscore the superior performance of the Transformer models and highlight the importance of diverse clinical and network features.

Paul Ryan, Harshvardhan J. Pandit, Rob Brennan

A core requirement for GDPR compliance is the maintenance of a register of processing activities (ROPA). Our analysis of six ROPA templates from EU data protection regulators shows the scope and granularity of a ROPA is subject to widely varying guidance in different jurisdictions. We present a consolidated data model based on common concepts and relationships across analysed templates. We then analyse the extent of using the Data Privacy Vocabulary - a vocabulary specification for GDPR. We show that the DPV currently does not provide sufficient concepts to represent the ROPA data model and propose an extension to fill this gap. This will enable creation of a pan-EU information management framework for interoperability between organisations and regulators for GDPR compliance.