Ivo Gollini Navarette

Mohamed bin Zayed University of Artificial Intelligence

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Younes Balah

Mohamed bin Zayed University of Artificial Intelligence

Abdulmotaleb El Saddik

University of Ottawa

Wong Yu Kang

Mohamed bin Zayed University of Artificial Intelligence

Toluwani Aremu

Mohamed bin Zayed University of Artificial Intelligence

Maryam Nadeem

Mohamed bin Zayed University of Artificial Intelligence

Ameera Ali Bawazir

Mohammad Yaqub

Mohamed bin Zayed University of Artificial Intelligence

Ibrahim Almakky

Mohamed bin Zayed University of Artificial Intelligence

Muhammad Ridzuan

Mohamed bin Zayed University of Artificial Intelligence

Cooperative Institutions

Mohamed bin Zayed University of Artificial Intelligence

University of Ottawa

King Fahd University of Petroleum and Minerals

Intelligent Health (United Kingdom)

University of Chittagong

University of Oxford

American Jewish Committee

University of Amsterdam

Science Oxford

Institute of Biomedical Science

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ScholarFace: Scanning Faces, Discovering Minds

Wong Yu Kang Toluwani Aremu Younes Balah Maryam Nadeem Ivo Gollini Navarette

In today’s data-driven world, quick access to scholarly info is vital. However, current academic search engines face challenges such as restricted text-based searching, uncertainties related to researcher names, absence of contact details, and lack of profile summaries. To mitigate these issues, we introduce ScholarFace, an innovative concept that could transform how we search for academic knowledge. It uses face recognition and language generation technology to spot scholars in photos effortlessly, giving us rich profiles and summaries of their work. It also offers an interactive chat element for users to get more insights about a scholar, reducing online search efforts. We take privacy and ethics issues seriously and ensure ScholarFace complies with rules and regulations. With ScholarFace, we hope to create a smarter, effortless, more connected scholarly world.

10.36227/techrxiv.170290975.55367595/v1

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ScholarFace: Scanning Faces, Discovering Minds

2023 IEEE International Conference on Consumer Electronics (ICCE) (2024)

Wong Yu Kang Toluwani Aremu Younes Balah Maryam Nadeem Ivo Gollini Navarette

In today's data-driven world, quick access to scholarly info is vital. However, current academic search engines face challenges such as restricted text-based searching, uncertainties related to researcher names, absence of contact details, and lack of profile summaries. To mitigate these issues, we introduce ScholarFace, an innovative concept that could transform how we search for academic knowledge. It uses face recognition and language generation technology to spot scholars in photos effortlessly, giving us rich profiles and summaries of their work. It also offers an interactive chat element for users to get more insights about a scholar, reducing online search efforts. We take privacy and ethics issues seriously and ensure ScholarFace complies with rules and regulations. With ScholarFace, we hope to create a smarter, effortless, more connected scholarly world.

10.1109/icce59016.2024.10444350

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Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays

arXiv (Cornell University) (2022)

Muhammad Ridzuan Ameera Ali Bawazir Ivo Gollini Navarette Ibrahim Almakky Mohammad Yaqub

Quick and accurate diagnosis is of paramount importance to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we investigate the impact of multi-task learning (classification and segmentation) on the ability of CNNs to differentiate between various appearances of COVID-19 infections in the lung. We also employ self-supervised pre-training approaches, namely MoCo and inpainting-CXR, to eliminate the dependence on expensive ground truth annotations for COVID-19 classification. Finally, we conduct a critical evaluation of the models to assess their deploy-readiness and provide insights into the difficulties of fine-grained COVID-19 multi-class classification from chest X-rays.

2019-20 coronavirus outbreak

10.48550/arxiv.2201.06052

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