Awadelrahman Mohamedelsadig Ali Ahmed

• Ahmed, Awadelrahman Mohamedelsadig Ali & Eliassen, Frank (2026). Causal inference framework for smart grids with application to demand response. Applied Energy. ISSN 0306-2619. 424. doi: 10.1016/j.apenergy.2026.128495. Fulltekst i vitenarkiv
• Jha, Debesh; Sharma, Vanshali; Banik, Debapriya; Bhattacharya, Debayan; Roy, Kaushiki & Hicks, Steven [Vis alle 33 forfattere av denne artikkelen] (2025). Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges. Medical Image Analysis. ISSN 1361-8415. 99. doi: 10.1016/j.media.2024.103307. Fulltekst i vitenarkiv Vis sammendrag

  Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm’s accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm’s prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the “Medico automatic polyp segmentation (Medico 2020)” and “MedAI: Transparency in Medical Image Segmentation (MedAI 2021)” competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a routine clinical examination. For the instrument segmentation task, the best team obtained a mean Intersection over union metric of 0.9364. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models’ credibility for clinical deployment. The best team obtained a final transparency score of 21 out of 25. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage subjective evaluation for building more transparent and understandable AI-based colonoscopy systems. Moreover, we discuss the need for multi-center and out-of-distribution testing to address the current limitations of the methods to reduce the cancer burden and improve patient care.

• Ahmed, Awadelrahman Mohamedelsadig Ali; Eliassen, Frank & Zhang, Yan (2023). Combinatorial Auctions and Graph Neural Networks for Local Energy Flexibility Markets, Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 23-23 Octorber, 2023. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350396782. s. 1–6. doi: 10.1109/ISGTEUROPE56780.2023.10407292. Fulltekst i vitenarkiv Vis sammendrag

  This paper proposes a new combinatorial auction framework for local energy flexibility markets, which addresses the issue of prosumers’ inability to bundle multiple flexibility time intervals. To solve the underlying NP-complete winner determination problems, we present a simple yet powerful heterogeneous tri-partite graph representation and design graph neural network-based models. Our models achieve an average optimal value deviation of less than 5% from an off-the-shelf optimization tool and show linear inference time complexity compared to the exponential complexity of the commercial solver. Contributions and results demonstrate the potential of using machine learning to efficiently allocate energy flexibility resources in local markets and solving optimization problems in general.

• Ahmed, Awadelrahman Mohamedelsadig Ali & Ali, Leen A. M. (2021). Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation. Nordic Machine Intelligence (NMI). 1(MedAI: Transparency in Medical Image Segmentation), s. 20–22. doi: 10.5617/nmi.9126. Fulltekst i vitenarkiv
• Ahmed, Awadelrahman Mohamedelsadig Ali (2020). Generative Adversarial Networks for Automatic Polyp Segmentation. CEUR Workshop Proceedings. Fulltekst i vitenarkiv Vis sammendrag

  This paper aims to contribute in bench-marking the automatic polyp segmentation problem using generative adversarial networks framework. Perceiving the problem as an image-to-image translation task, conditional generative adversarial networks are utilized to generate masks conditioned by the images as inputs. Both generator and discriminator are convolution neural networks based. The model achieved 0.4382 on Jaccard index and 0.611 as F2 score

• Ahmed, Awadelrahman Mohamedelsadig Ali; Zhang, Yan & Eliassen, Frank (2020). Generative Adversarial Networks and Transfer Learning for Non-Intrusive Load Monitoring in Smart Grids, 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE International Conference on Smart Grid Communications (SmartGridComm). ISSN 9781728161273. doi: 10.1109/SmartGridComm47815.2020.9302933. Fulltekst i vitenarkiv Vis sammendrag

  Abstract—Non-intrusive load monitoring (NILM) objective is to disaggregate the total power consumption of a building into individual appliance-level profiles. This gives insights to consumers to efficiently use energy and realizes smart grid efficiency outcomes. While many studies focus on achieving accurate models, few of them address the models generalizability. This paper proposes two approaches based on generative adversarial networks to achieve high-accuracy load disaggregation. Concurrently, the paper addresses the model generalizability in two ways, the first is by transfer learning by parameter sharing and the other is by learning compact common representations between source and target domains. This paper also quantitatively evaluate the worth of these transfer learning approaches based on the similarity between the source and target domains. The models are evaluated on three open-access datasets and outperformed recent machine learning methods.

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• Ahmed, Awadelrahman Mohamedelsadig Ali (2021). An Empirical Analysis of Transfer Learning for Generative Adversarial Networks. Fulltekst i vitenarkiv Vis sammendrag

  Generative adversarial networks framework immensely attracted the machine learning community attention in the recent years. GANs have succeeded in generating realistic-looking data from noise and have countless applications. On the other hand, Transfer Learning is a smart way that aims to enhance machine learning models in the case of scarce data. The main intuition behind transfer learning is to train models on a plenty of data that is “not” our targeted one but has some similar attributes to it. Then transfer learning in models that contains single networks, such as CNNs, are implemented by sharing the first layers (from the input side) of the model between source and target domains and initialize other layers then retrain the model on our scarce target data. This allows the model to have a good start when learning on the target data and achieve better results than if we trained it from scratch on the target. Now, GANs framework consists of two networks, a generator and a discriminator, and a legit question here is: which network and which part of that network contains the transferable features and hence parameter? Should we fine-tune or freeze the shared parameters? I am trying to answer this specific and direct question by showing imperial results of a series of experiments that I designed based on conditional GANs framework.

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