Amir Atapour-Abarghouei
@durham.ac.uk
Scopus Publications
- Multimodal models for skin cancer classification using clinical freetext and dermatoscopic images
Matthew Watson, Thomas Winterbottom, Thomas Hudson, Benedict Jones, Hubert P. H. Shum, Amir Atapour-Abarghouei, Toby Breckon, James Harmsworth King, Noura Al Moubayed
Communications Medicine, 2026
Skin cancer is one of the most prevalent cancers globally, with early detection critical to ensure reduced mortality risk. To aid early detection, machine learning (ML) skin cancer detection models have been proposed, currently with a focus on dermatoscopic imaging only. However, freetext may provide extra diagnostic information that is not present in images alone. We constructed a multimodal dataset comprising 5481 dermatoscopic images from 4538 patients, including patient metadata and clinical notes, with binary labels (benign vs. malignant, 7% malignant). To assess and mitigate bias from leading language, we developed a clinical text preprocessing pipeline combining regular expressions and large language models, enabling multiple levels of filtering. We train multimodal ML models on this dataset to explore the effect of freetext on model performance. Our results show that incorporating unfiltered text significantly improves classification performance (0.970 AUROC) compared to visual data alone (0.909 AUROC); even with leading language removed, performance gains persist (0.948 AUROC). This work benchmarks clinical freetext inclusion in skin lesion classification, demonstrating that clinical text contributes predictive value beyond that available in images alone. The model’s high performance on unfiltered clinical text highlights the high levels of bias, and possible shortcutting, present in this text which may make it unsuitable for inclusion in some ML models. By systematically filtering clinical notes via our proposed technique, we show that multimodal models retain improved accuracy while reducing bias. These results provide practical guidance for integrating clinical text into real-world skin cancer detection systems and establish a foundation for future multimodal research in dermatology. Prompt detection of skin cancer improves survival, but diagnosis must be made by clinicians. Image-based machine learning models for skin cancer classification have shown promise. However, key information is often only recorded in clinical notes, such as whether a lesion has changed, itches, or bleeds. By creating a dataset that contains images, patient data, and freetext descriptions of the problem, we train a series of machine learning models on both images and freetext to predict skin cancer. We show that the inclusion of freetext significantly enhances model performance, but that care must be taken to ensure the freetext does not unintentionally bias the model. These models could be used in multiple points in a skin cancer clinical workflow to either support more accurate referrals to dermatology, or direct patient access to dermatology services, potentially reducing wait times and improving patient outcomes. Watson et al. explore multimodal machine learning models for lesion classification, using dermatoscopic images, freetext, and patient metadata; they investigate how leading language in freetext affects model bias, and introduce methods to address this. Their results show freetext improves model performance even with the leading language removed. - Exploring the Potentials of Spiking Neural Networks for Image Deraining
Shuang Chen, Tom Krajnik, Farshad Arvin, Amir Atapour-Abarghouei
Proceedings of the Aaai Conference on Artificial Intelligence, 2026
Biologically plausible and energy-efficient frameworks such as Spiking Neural Networks (SNNs) have not been sufficiently explored in low-level vision tasks. Taking image deraining as an example, this study addresses the representation of the inherent high-pass characteristics of spiking neurons, specifically in image deraining and innovatively proposes the Visual LIF (VLIF) neuron, overcoming the obstacle of lacking spatial contextual understanding present in traditional spiking neurons. To tackle the limitation of frequency-domain saturation inherent in conventional spiking neurons, we leverage the proposed VLIF to introduce the Spiking Decomposition and Enhancement Module and the lightweight Spiking Multi-scale Unit for hierarchical multi-scale representation learning. Extensive experiments across five benchmark deraining datasets demonstrate that our approach significantly outperforms state-of-the-art SNN-based deraining methods, achieving this superior performance with only 13% of their energy consumption. These findings establish a solid foundation for deploying SNNs in high-performance, energy-efficient low-level vision tasks. - Neural reranking for UK statutory retrieval: Provision-level evaluation and an open distilled model
Amal Saad Alshehri, Can Eken, Nelly Bencomo, Amir Atapour-Abarghouei
Artificial Intelligence and Law, 2026
This work explores provision-level retrieval and neural reranking for UK primary and secondary legislation. We introduce UK-StatuteCorpus , a corpus of recent UK Acts and statutory instruments from legislation.gov.uk , together with a 100-query evaluation set of practitioner-style questions whose graded relevance judgements distinguish legally operative, supporting and contextual provisions. Using BM25 and an MPNet-based dense retriever to build candidate sets, we evaluate ten neural rerankers, including transformer cross-encoders, a late-interaction reranker, an LLM-based listwise reranker and proprietary APIs. Across both sparse and dense pools, neural reranking consistently improves normalized Discounted Cumulative Gain (nDCG) and Mean Reciprocal Rank (MRR) over first-stage retrieval. We further distil a proprietary Voyage reranker into a ModernBERT-based cross-encoder, Distilled-Voyage-ModernBERT, which approaches the teacher’s effectiveness and outperforms other open rerankers on our benchmark. Results are based on 100 expert-validated queries, each linked to three graded provisions from a single UK instrument, so they characterise single-instrument, provision-level retrieval over recent UK legislation. - MDCF-Net: Modality Decomposition and Compensation Fusion Network for Infrared-Visible Object Detection
Jiangtao Fan, Zeyu Xiao, Anish Jindal, Amir Atapour-Abarghouei
Frontiers in Artificial Intelligence and Applications, 2025
Infrared-visible object detection aims to leverage the complementary information between infrared and visible modalities to improve detection performance in challenging environments. However, existing infrared-visible object detection methods face several limitations: (1) difficulty in effectively extracting and decomposing modality-common and modality-specific features; (2) interference from modality-irrelevant or redundant information; and (3) insufficient fusion of cross-modal complementary cues. To address these issues, we propose a novel Modality Decomposition and Compensation Fusion Network (MDCF-Net). Specifically, MDCF-Net first decomposes the common and unique features across different modalities. It then performs selective enhancement and interaction between these features via cross-modality compensation. Finally, a dynamic fusion strategy based on spatial and channel attention is applied to adaptively integrate the enhanced features. Extensive experiments on two public datasets, LLVIP and FLIR, demonstrate that our proposed method achieves superior detection performance and exhibits robust generalisation across various challenging conditions. The Code is available at https://github.com/fanjiangtao666/MDCF-Net/tree/main - Multi-scale Efficient Spatial Attention on Human Activity Recognition Using Wearable Sensors
Jiangtao Fan, Anish Jindal, Amir Atapour-Abarghouei
Communications in Computer and Information Science, 2025 - BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion
Sike Xiang, Shuang Chen, Amir Atapour-Abarghouei
Emnlp 2025 2025 Conference on Empirical Methods in Natural Language Processing Findings of Emnlp 2025, 2025
As multimodal large language models (MLLMs) advance, their large-scale architectures pose challenges for deployment in resource-constrained environments.In the age of large models, where energy efficiency, computational scalability and environmental sustainability are paramount, the development of lightweight and high-performance models is critical for real-world applications.As such, we propose a lightweight MLLM framework for end-to-end visual question answering.Our proposed approach centres on BreezeCLIP, a compact yet powerful vision-language encoder optimised for efficient multimodal understanding.With only 1.2 billion parameters overall, our model significantly reduces computational cost while achieving performance comparable to standard-size MLLMs.Experiments conducted on multiple datasets further validate its effectiveness in balancing accuracy and efficiency.The modular and extensible design enables generalisation to broader multimodal tasks.The proposed lightweight vision-language framework is denoted as BcQLM (BreezeCLIPenhanced Q-Gated Multimodal Language Model).It offers a promising path toward deployable MLLMs under practical hardware constraints. - Beyond Syntax: How Do LLMs Understand Code?
Marc North, Amir Atapour-Abarghouei, Nelly Bencomo
Proceedings International Conference on Software Engineering, 2025
Within software engineering research, Large Language Models (LLMs) are often treated as ‘black boxes’, with only their inputs and outputs being considered. In this paper, we take a machine interpretability approach to examine how LLMs internally represent and process code.We focus on variable declaration and function scope, training classifier probes on the residual streams of LLMs as they process code written in different programming languages to explore how LLMs internally represent these concepts across different programming languages. We also look for specific attention heads that support these representations and examine how they behave for inputs of different languages.Our results show that LLMs have an understanding — and internal representation — of language-independent coding semantics that goes beyond the syntax of any specific programming language, using the same internal components to process code, regardless of the programming language that the code is written in. Furthermore, we find evidence that these language-independent semantic components exist in the middle layers of LLMs and are supported by language-specific components in the earlier layers that parse the syntax of specific languages and feed into these later semantic components.Finally, we discuss the broader implications of our work, particularly in relation to concerns that AI, with its reliance on large datasets to learn new programming languages, might limit innovation in programming language design. By demonstrating that LLMs have a language-independent representation of code, we argue that LLMs may be able to flexibly learn the syntax of new programming languages while retaining their semantic understanding of universal coding concepts. In doing so, LLMs could promote creativity in future programming language design, providing tools that augment rather than constrain the future of software engineering. - SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM
Shuang Chen, Haozheng Zhang, Amir Atapour–Abarghouei, Hubert P.H. Shum
Proceedings 2025 IEEE Winter Conference on Applications of Computer Vision Wacv 2025, 2025
Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions to exhibit similar patterns to the semanticly consistent regions. This requires a model with a strong capacity to capture long-range dependencies. Existing models struggle in this regard due to the slow growth of receptive field for Convolutional Neural Networks (CNNs) based methods and patch-level interactions in Transformer-based methods, which are ineffective for capturing long-range dependencies. Motivated by this, we propose SEM-Net, a novel visual State Space model (SSM) vision network, modelling corrupted images at the pixel level while capturing long-range dependencies (LRDs) in state space, achieving a linear computational complexity. To address the inherent lack of spatial awareness in SSM, we introduce the Snake Mamba Block (SMB) and Spatially-Enhanced Feed-forward Network. These innovations enable SEM-Net to outperform state-of-the-art inpainting methods on two distinct datasets, showing significant improvements in capturing LRDs and enhancement in spatial consistency. Additionally, SEM-Net achieves state-of-the-art performance on motion deblurring, demonstrating its generalizability. Our source code is available: https://github.com/ChrisChenl023/SEM-Net. - Evaluating Deep Graph Network Performance by Augmenting Node Features with Structural Features
Mohamad Abushofa, Amir Atapour-Abarghouei, Matthew Forshaw, A. Stephen McGough
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2025 - TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions
Yixin Sun, Li Li, Wenke E, Amir Atapour-Abarghouei, Toby P. Breckon
Proceedings of the International Joint Conference on Neural Networks, 2025
Detecting traversable pathways in unstructured out-door environments remains a significant challenge for autonomous robots, especially in critical applications such as wide-area search and rescue, as well as in incident management scenarios such as forest fires. Current datasets and models primarily focus on either urban environments or wide vehicle-traversable off-road tracks, leaving a substantial gap in tackling the complexities of trail-based off-road scenarios. To address this issue, we introduce the Trail-based Off-road Multimodal Dataset (TOMD), a comprehensive dataset explicitly designed for narrow and unstructured trail-like environments. Our dataset features high-fidelity multimodal sensor data — including 128-channel LiDAR, stereo imagery, GNSS, IMU, and illumination measurements — collected through repeated runs across di-verse environmental conditions. In addition, we propose a novel dynamic multiscale data fusion model for precise traversable pathway prediction in trail-like areas. The study investigates the impact of various fusion processes — early, cross, and mixed — on model performance under different illumination levels: low-light, normal ambient lighting, and bright conditions. The results highlight the effectiveness of our approach, variation in performance across illumination levels, and the potential applicability of the dataset in diverse environmental conditions.Our work provides a valuable resource for advancing trail-based off-road navigation, and we openly publish our TOMD at https://github.com/yyyxs1125/TMOD to establish a future bench-mark in this research domain. - FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment
Ruisheng Han, Kanglei Zhou, Amir Atapour-Abarghouei, Xiaohui Liang, Hubert P.H. Shum
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2025 - Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots
Shuang Chen, Yifeng He, Barry Lennox, Farshad Arvin, Amir Atapour-Abarghouei
Proceedings IEEE International Conference on Robotics and Automation, 2025 - Dur360BEV: A Real-World 360-Degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving
Wenke E, Chao Yuan, Li Li, Yixin Sun, Yona Falinie A. Gaus, Amir Atapour-Abarghouei, Toby P. Breckon
Proceedings IEEE International Conference on Robotics and Automation, 2025 - HINT: High-Quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention
Shuang Chen, Amir Atapour-Abarghouei, Hubert P. H. Shum
IEEE Transactions on Multimedia, 2024 - Code Gradients: Towards Automated Traceability of LLM-Generated Code
Marc North, Amir Atapour-Abarghouei, Nelly Bencomo
Proceedings of the IEEE International Conference on Requirements Engineering, 2024 - Diagnosis of Multiple Sclerosis by Detecting Asymmetry Within the Retina Using a Similarity-Based Neural Network
Regan Cain Bolton, Rahele Kafieh, Fereshteh Ashtari, Amir Atapour-Abarghouei
IEEE Access, 2024 - MxT: Mamba x Transformer for Image Inpainting
35th British Machine Vision Conference Bmvc 2024, 2024 - Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics
Seyma Yucer, Amir Atapour Abarghouei, Noura Al Moubayed, Toby P. Breckon
Proceedings of the International Joint Conference on Neural Networks, 2024 - Insights from the Use of Previously Unseen Neural Architecture Search Datasets
Rob Geada, David Towers, Matthew Forshaw, Amir Atapour-Abarghouei, A. Stephen McGough
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2024 - FEGR: Feature Enhanced Graph Representation Method for Graph Classification
Mohamad Elhadi Abushofa, Amir Atapour Abarghouei, Matthew Forshaw, Andrew Stephen Mcgough
Proceedings of the 2023 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining Asonam 2023, 2023 - INCLG: Inpainting for non-cleft lip generation with a multi-task image processing network[Formula presented]
Shuang Chen, Amir Atapour-Abarghouei, Edmond S.L. Ho, Hubert P.H. Shum
Software Impacts, 2023 - Differentiating Glaucomatous Optic Neuropathy From Non-glaucomatous Optic Neuropathies Using Deep Learning Algorithms
Mahsa Vali, Massood Mohammadi, Nasim Zarei, Melika Samadi, Amir Atapour-Abarghouei, Wasu Supakontanasan, Yanin Suwan, Prem S. Subramanian, Neil R. Miller, Rahele Kafieh, Masoud Aghsaei Fard
American Journal of Ophthalmology, 2023 - Predicting the Performance of a Computing System with Deep Networks
Mehmet Cengiz, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough
Icpe 2023 Proceedings of the 2023 ACM Spec International Conference on Performance Engineering, 2023 - Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery
Yona Falinie A. Gaus, Neelanjan Bhowmik, Brian K. S. Isaac-Medina, Hubert P. H. Shum, Amir Atapour-Abarghouei, Toby P. Breckon
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2023 - Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
Proceedings of Machine Learning Research, 2022 - Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification
Peter J. Bevan, Amir Atapour-Abarghouei
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2022 - Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets
Michael Luke Battle, Amir Atapour-Abarghouei, Andrew Stephen McGough
Proceedings 2022 IEEE International Conference on Big Data Big Data 2022, 2022 - A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
Shuang Chen, Amir Atapour-Abarghouei, Jane Kerby, Edmond S. L. Ho, David C. G. Sainsbury, Sophie Butterworth, Hubert P. H. Shum
Bhi Bsn 2022 IEEE EMBS International Conference on Biomedical and Health Informatics and IEEE EMBS International Conference on Wearable and Implantable Body Sensor Networks Symposium Proceedings, 2022 - Transforming Fake News: Robust Generalisable News Classification Using Transformers
Ciara Blackledge, Amir Atapour-Abarghouei
Proceedings 2021 IEEE International Conference on Big Data Big Data 2021, 2021 - Just Drive: Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving
Jack Stelling, Amir Atapour-Abarghouei
Proceedings 2021 IEEE International Conference on Big Data Big Data 2021, 2021 - Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking
Steven Carrell, Amir Atapour-Abarghouei
Proceedings 2021 IEEE International Conference on Big Data Big Data 2021, 2021 - Rank over Class: The Untapped Potential of Ranking in Natural Language Processing
Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough
Proceedings 2021 IEEE International Conference on Big Data Big Data 2021, 2021 - Resolving the cybersecurity Data Sharing Paradox to scale up cybersecurity via a co-production approach towards data sharing
Amir Atapour-Abarghouei, A. Stephen McGough, David S. Wall
Proceedings 2020 IEEE International Conference on Big Data Big Data 2020, 2020 - Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks
John Brennan, Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson, Boguslaw Obara, Andrew Stephen McGough
Proceedings 2020 IEEE International Conference on Big Data Big Data 2020, 2020 - Domain Adaptation via Image Style Transfer
Amir Atapour-Abarghouei, Toby P. Breckon
Domain Adaptation in Computer Vision with Deep Learning, 2020 - On the impact of lossy image and video compression on the performance of deep convolutional neural network architectures
Matt Poyser, Amir Atapour-Abarghouei, Toby P. Breckon
Proceedings International Conference on Pattern Recognition, 2020 - Leveraging synthetic subject invariant EEG signals for zero calibration BCI
Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner, Jason D. Connolly, Toby P. Breckon
Proceedings International Conference on Pattern Recognition, 2020 - Volenti non fit injuria: Ransomware and its Victims
Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough
Proceedings 2019 IEEE International Conference on Big Data Big Data 2019, 2019 - A King's Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation
Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough
Proceedings 2019 IEEE International Conference on Big Data Big Data 2019, 2019 - Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions
Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson, John Brennan, Ibad Kureshi, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara
Proceedings 2019 IEEE International Conference on Big Data Big Data 2019, 2019 - To Complete or to Estimate, That is the Question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation
Amir Atapour-Abarghouei, Toby P. Breckon
Proceedings 2019 International Conference on 3D Vision 3dv 2019, 2019 - Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior
Amir Atapour-Abarghouei, Toby P. Breckon
Proceedings International Conference on Image Processing Icip, 2019 - Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon
Proceedings of the International Joint Conference on Neural Networks, 2019 - Generative adversarial framework for depth filling via Wasserstein metric, cosine transform and domain transfer
Amir Atapour-Abarghouei, Samet Akcay, Grégoire Payen de La Garanderie, Toby P. Breckon
Pattern Recognition, 2019 - Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification
Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner, Jason D. Connolly, Noura Al Moubayed, Toby P. Breckon
Proceedings of the International Joint Conference on Neural Networks, 2019 - Veritatem dies aperit-Temporally consistent depth prediction enabled by a multi-task geometric and semantic scene understanding approach
Amir Atapour-Abarghouei, Toby P. Breckon
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019 - Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation
Amir Atapour-Abarghouei, Toby P. Breckon
Advances in Computer Vision and Pattern Recognition, 2019 - GANomaly: Semi-supervised Anomaly Detection via Adversarial Training
Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon
Lecture Notes in Computer Science, 2019 - Real-Time Monocular Depth Estimation Using Synthetic Data with Domain Adaptation via Image Style Transfer
Amir Atapour-Abarghouei, Toby P. Breckon
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018 - A comparative review of plausible hole filling strategies in the context of scene depth image completion
Amir Atapour-Abarghouei, Toby P. Breckon
Computers and Graphics Pergamon, 2018 - Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion
Amir Atapour-Abarghouei, Toby P. Breckon
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018 - Eliminating the blind spot: Adapting 3D object detection and monocular depth estimation to 360 ° Panoramic Imagery
Grégoire Payen de La Garanderie, Amir Atapour Abarghouei, Toby P. Breckon
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018 - DepthComp: Real-time depth image completion based on prior semantic scene segmentation
British Machine Vision Conference 2017 Bmvc 2017, 2017 - Back to Butterworth - A Fourier basis for 3D surface relief hole filling within RGB-D imagery
Amir Atapour-Abarghouei, Gregoire Payen de La Garanderie, Toby P. Breckon
Proceedings International Conference on Pattern Recognition, 2016 - Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
Afshin Ghanizadeh, Amir Atapour Abarghouei, Saman Sinaie, Puteh Saad, Siti Mariyam Shamsuddin
Applied Optics, 2011 - A robust fuzzy and Cellular Learning Automata edge detection and enhancement method
Proceedings of the 2010 International Conference on Image Processing Computer Vision and Pattern Recognition Ipcv 2010, 2010 - Notice of Retraction: A fuzzy-particle swarm optimization based algorithm for solving shortest path problem
Afshin Ghanizadeh, Saman Sinaie, Amir Atapour Abarghouei, Siti Mariyam Shamsuddin
Iccet 2010 2010 International Conference on Computer Engineering and Technology Proceedings, 2010 - A modified PSO method enhanced with fuzzy inference system for solving the planar Graph Coloring problem
Proceedings of the 2010 International Conference on Artificial Intelligence Icai 2010, 2010 - A survey of pattern recognition applications in cancer diagnosis
Amir Atapour Abarghouei, Afshin Ghanizadeh, Saman Sinaie, Siti Mariyam Shamsuddin
Socpar 2009 Soft Computing and Pattern Recognition, 2009 - Advances of soft computing methods in edge detection
International Journal of Advances in Soft Computing and Its Applications, 2009
RECENT SCHOLAR PUBLICATIONS
- Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation
S Xiang, S Chen, KQ Lin, J Yu, Y Sun, P Torr, A Atapour-Abarghouei
arXiv preprint arXiv:2605.11533 , 2026
2026 - UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement
S Chen, R Li, Z Zhu, R Thenius, F Arvin, A Atapour-Abarghouei
arXiv preprint arXiv:2605.08376 , 2026
2026 - Leveraging Imperfect Medical Data: A Manifold-Consistent Spatio-Temporal Network for Sensor-based Human Activity Recognition
J Fan, A Jindal, A Atapour-Abarghouei
arXiv preprint arXiv:2605.00913 , 2026
2026 - Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
G Alosaimi, H Alhamdan, S Katsigiannis, A Atapour-Abarghouei, ...
arXiv preprint arXiv:2604.19368 , 2026
2026 - Motion-Adaptive Multi-Scale Temporal Modelling with Skeleton-Constrained Spatial Graphs for Efficient 3D Human Pose Estimation
R Li, S Chen, F Arvin, A Atapour-Abarghouei
arXiv preprint arXiv:2604.03652 , 2026
2026 - ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations
R Li, Z Chang, J Hu, J Li, A Atapour-Abarghouei, HPH Shum
arXiv preprint arXiv:2604.03649 , 2026
2026 - VRUD: A Drone Dataset for Complex Vehicle-VRU Interactions within Mixed Traffic
Z Wang, H Kou, C Wang, R Li, HPH Shum, A Atapour-Abarghouei, ...
arXiv preprint arXiv:2604.01134 , 2026
2026 - CBANet: A Compact Attention-Based CNN–BiLSTM Network for Aggressive Driving Event Detection
H Alhamdan, G Alosaimi, A Atapour-Abarghouei, F Arvin
2026 - Multimodal models for skin cancer classification using clinical freetext and dermatoscopic images
M Watson, T Winterbottom, T Hudson, B Jones, HPH Shum, ...
Communications Medicine , 2026
2026 - Exploring the Potentials of Spiking Neural Networks for Image Deraining
S Chen, T Krajnik, F Arvin, A Atapour-Abarghouei
Proceedings of the AAAI Conference on Artificial Intelligence 40 (4), 3029-3037 , 2026
2026
Citations: 1 - EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover
G Alosaimi, M Alsayyari, Y Sun, S Katsigiannis, A Atapour-Abarghouei, ...
arXiv preprint arXiv:2602.20041 , 2026
2026 - Neural reranking for UK statutory retrieval: Provision-level evaluation and an open distilled model
AS Alshehri, C Eken, N Bencomo, A Atapour-Abarghouei
Artificial Intelligence and Law, 1-31 , 2026
2026 - KD360-VoxelBEV: LiDAR and 360-degree Camera Cross Modality Knowledge Distillation for Bird's-Eye-View Segmentation
Y Sun, J Liu, HPH Shum, A Atapour-Abarghouei, TP Breckon
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer … , 2026
2026 - CaFlow: Enhancing Long-Term Action Quality Assessment with Causal Counterfactual Flow
R Han, K Zhou, S Chen, A Atapour-Abarghouei, HPH Shum
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer … , 2026
2026
Citations: 2 - DEEP-SEA: Deep-Learning Enhancement for Environmental Perception in Submerged Aquatics
S Chen, R Thenius, F Arvin, A Atapour-Abarghouei
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems … , 2025
2025
Citations: 1 - BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion
S Xiang, S Chen, A Atapour-Abarghouei
arXiv preprint arXiv:2509.08715 , 2025
2025 - Using artificial intelligence in the analysis of CT scans of the axillary nodes in breast cancer: a systematic review
J Cox, MA Bhatti, A Atapour-Abarghouei
European Journal of Radiology Artificial Intelligence, 100040 , 2025
2025
Citations: 2 - Deep learning-enhanced visual monitoring in hazardous underwater environments with a swarm of micro-robots
S Chen, Y He, B Lennox, F Arvin, A Atapour-Abarghouei
2025 IEEE International Conference on Robotics and Automation (ICRA), 563-569 , 2025
2025
Citations: 1 - Beyond Syntax: How Do LLMs Understand Code?
M North, A Atapour-Abarghouei, N Bencomo
2025 IEEE/ACM 47th International Conference on Software Engineering: New … , 2025
2025
Citations: 3 - DurTOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions
Y Sun, L Li, A Atapour-Abarghouei, TP Breckon
2025
Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
- GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
S Akcay, A Atapour-Abarghouei, TP Breckon
arXiv preprint arXiv:1805.06725 , 2018
2018
Citations: 2533 - Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection
S Akçay, A Atapour-Abarghouei, TP Breckon
2019 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2019
2019
Citations: 638 - Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer
A Atapour-Abarghouei, TP Breckon
Proceedings of the IEEE Conference on Computer Vision and Pattern … , 2018
2018
Citations: 365 - Style augmentation: data augmentation via style randomization.
PTG Jackson, AA Abarghouei, S Bonner, TP Breckon, B Obara
CVPR Workshops 6, 10-11 , 2019
2019
Citations: 285 - Simulating brain signals: Creating synthetic eeg data via neural-based generative models for improved ssvep classification
NKN Aznan, A Atapour-Abarghouei, S Bonner, JD Connolly, ...
2019 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2019
2019
Citations: 176 - Eliminating the blind spot: Adapting 3d object detection and monocular depth estimation to 360 panoramic imagery
GP de La Garanderie, AA Abarghouei, TP Breckon
Proceedings of the European Conference on Computer Vision (ECCV), 789-807 , 2018
2018
Citations: 120 - HINT: High-quality inpainting transformer with mask-aware encoding and enhanced attention
S Chen, A Atapour-Abarghouei, HPH Shum
IEEE Transactions on Multimedia 26, 7649-7660 , 2024
2024
Citations: 78 - Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion
PJ Bevan¹, A Atapour-Abarghouei
Domain Adaptation and Representation Transfer: 4th MICCAI Workshop, DART … , 2022
2022
Citations: 66 - On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures
M Poyser, A Atapour-Abarghouei, TP Breckon
2020 25th International Conference on Pattern Recognition (ICPR), 2830-2837 , 2021
2021
Citations: 59 - Advances of soft computing methods in edge detection
AA Abarghouei, A Ghanizadeh, SM Shamsuddin
Int. J. Advance. Soft Comput. Appl 1 (2), 162-203 , 2009
2009
Citations: 57 - A comparative review of plausible hole filling strategies in the context of scene depth image completion
A Atapour-Abarghouei, TP Breckon
Computers & Graphics 72, 39-58 , 2018
2018
Citations: 53 - Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments
BG Maciel-Pearson, S Akçay, A Atapour-Abarghouei, C Holder, ...
IEEE Robotics and Automation Letters 4 (4), 4116-4123 , 2019
2019
Citations: 48 - Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments
BG Maciel-Pearson, L Marchegiani, S Akcay, A Atapour-Abarghouei, ...
arXiv preprint arXiv:1912.05684 , 2019
2019
Citations: 45 - Veritatem dies aperit-temporally consistent depth prediction enabled by a multi-task geometric and semantic scene understanding approach
A Atapour-Abarghouei, TP Breckon
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern … , 2019
2019
Citations: 45 - Transforming Fake News: Robust Generalisable News Classification Using Transformers
C Blackledge, A Atapour-Abarghouei
2021 IEEE International Conference on Big Data (Big Data), 3960-3968 , 2021
2021
Citations: 41 - Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions
S Bonner, A Atapour-Abarghouei, PT Jackson, J Brennan, I Kureshi, ...
2019 IEEE International Conference on Big Data (Big Data), 5336-5345 , 2019
2019
Citations: 41 - DepthComp: real-time depth image completion based on prior semantic scene segmentation.
A Atapour-Abarghouei, TP Breckon
British Machine Vision Association (BMVA) , 2017
2017
Citations: 35 - Code Gradients: Towards Automated Traceability of LLM-Generated Code
M North, A Atapour-Abarghouei, N Bencomo
2024 IEEE 32nd International Requirements Engineering Conference (RE), 321-329 , 2024
2024
Citations: 33 - Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
P Bevan, A Atapour-Abarghouei
arXiv preprint arXiv:2109.09818 , 2021
2021
Citations: 33 - Generative adversarial framework for depth filling via Wasserstein metric, cosine transform and domain transfer
A Atapour-Abarghouei, S Akcay, GP de La Garanderie, TP Breckon
Pattern Recognition 91, 232-244 , 2019
2019
Citations: 32
