Triaging diffuse large B cell lymphoma and Burkitt lymphoma with AI in Malawi, using local patients and specimens for the development and testing of AI models
Open Access
- 11 November 2026
- journal article
- Published by BMJ in BMJ Connections Oncology
- Vol. 3 (1) , e000149
Abstract
No abstract availableFunding Information
- Center for Cancer Research (Intramural research program (ZIH BC 012032))
- NIH (P30CA016086)
- NIH (U54 CA254564)
This publication has 23 references indexed in Scilit:
- A whole-slide foundation model for digital pathology from real-world dataNature, 2024
- Towards a general-purpose foundation model for computational pathologyNature Medicine, 2024
- Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profilingSeminars in Cancer Biology, 2022
- DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image setScientific Data, 2021
- A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitalsNature Communications, 2020
- Scientific Advances and the Evolution of Diagnosis, Subclassification and Treatment of LymphomaArchives of Medical Research, 2020
- Risk factors for Burkitt lymphoma in East African children and minors: A case–control study in malaria‐endemic regions in Uganda, Tanzania and KenyaInternational Journal of Cancer, 2019
- Lymphoma and Pathology in Sub-Saharan AfricaClinics In Laboratory Medicine, 2018
- Practical Successes in Telepathology Experiences in AfricaClinics In Laboratory Medicine, 2018
- The use and interpretation of the Friedman test in the analysis of ordinal‐scale data in repeated measures designsPhysiotherapy Research International, 1996