Author

Nwabisa Mlandu

University of Cape TownORCID 0000-0002-3483-2898Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Assay:electronic health records· 1 Approach:experimental assay· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

Bokang Methola· 2 shared Candice Knipe· 2 shared Chloë A. JacobsiD· 2 shared Donna HerriD· 2 shared Khanyisa Nkubungu· 2 shared Kirsten Ann DonaldiD· 2 shared Lauren Davel· 2 shared Layla E. BradfordiD· 2 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersNeuroscience

Bokang Methola

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Author profile →

2 papers

Recent·Most cited

Deep learning super-resolution of paediatric ultra-low-field MRI without paired high-field scans

Ula Briski, Niall Bourke, Hajer Karoui, Kirsten Ann Donald, Layla E. Bradford, Simone R. Williams, Michal R. Zieff, Sadia Parkar, Sidra Kaleem, Salman Osmani, Sean Deoni, Steven Williams, Khula South Africa Data Collection Team, Donna Herr, Marlie Miles, , František Váša

This study develops a deep learning model to enhance ultra-low-field MRI scans to near high-field resolution without paired high-field data, improving image quality and accessibility.

Scientific Reports · Aug 26, 2026Peer-reviewedNeuroscience

Evaluating a Novel High-Density EEG Sensor Net Structure for Improving Inclusivity in Infants with Curly or Tightly Coiled Hair

Nwabisa Mlandu, Sarah A. McCormick, Lauren Davel, Michal R. Zieff, Layla E. Bradford, Donna Herr, Chloë A. Jacobs, Anele Khumalo, Candice Knipe, Zamazimba Madi, Thandeka Mazubane, Bokang Methola, Tembeka Mhlakwaphalwa, Marlie Miles, Zayaan Goolam Nabi, , Laurel Joy Gabard-Durnam

The paper tests two high-density EEG sensor net designs in infants with curly or tightly-coiled hair, finding that the tall design improves data quality and retention.

Developmental Cognitive Neuroscience · May 27, 2024Peer-reviewedNeuroscience