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Outputs (3)

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review (2023)
Journal Article
Rashid, A., Al-Obeidat, F., Kanthimathinathan, H. K., Benakatti, G., Hafez, W., Ramaiah, R., Brierley, J., Hanisch, B., Khilnani, P., Koutentis, C., Brusletto, B. S., Toufiq, M., Hussain, Z., Vyas, H., Malik, Z. A., Schumacher, M., Malik, R. A., Deshpande, S., Quraishi, N., Kadwa, R., …Hussain, A. (2024). Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review. Informatics in Medicine Unlocked, 44, Article 101419. https://doi.org/10.1016/j.imu.2023.101419

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted,... Read More about Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review.

MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification (2023)
Journal Article
Lu, L., Cui, X., Tan, Z., & Wu, Y. (2024). MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(4), 725-736. https://doi.org/10.1109/TCBB.2023.3284846

In the medical research domain, limited data and high annotation costs have made efficient classification under few-shot conditions a popular research area. This paper proposes a meta-learning framework, termed MedOptNet, for few-shot medical image c... Read More about MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification.