Hajra Malik: Bridging Language Gaps in Hematology With AI
Hajra Malik, Researcher – Medical AI and Vision-Language Models at Intelligent Machines Lab, shared a post on LinkedIn about a recent article she and her colleagues co-authored, published in NEJM, adding:
“Excited to share our paper, ‘Multilingual Hematology Visual Question Answering Dataset,’ published in Machine Learning for Biomedical Imaging as part of the MICCAI SIG Open Data 2026 special issue!
Medical vision-language models can answer questions about images, but hematology resources remain largely English-centric. In Pakistan, that creates a meaningful gap: medical information is often documented in English, while communication with patients commonly happens in Urdu or a mix of languages.
To help researchers study this challenge, we developed WBCMor-VQA: a clinically validated English-Urdu visual question answering benchmark focused on the morphology of normal and leukemic white blood cells.
It brings together approximately 110,000 bilingual question–answer pairs for 20,000 single-cell images, a domain-specific Urdu hematology dictionary, and baseline evaluations of open-source vision-language models.
I’m grateful to my co-authors Hafiza Tooba Aftab, Abdul Rehman, Mohsen Ali, Waqas Sultani, for their collaboration and guidance.
Thank you to Information Technology University, Lahore, and the Intelligent Machines lab ITU for supporting this work.
If you’re attending MICCAI Society 2026 in Strasbourg, I’d be glad to connect and talk about medical imaging, vision-language models, and multilingual healthcare AI.”
Title: Multilingual Hematology Visual Question Answering Dataset
Authors: Hajra Malik, Hafiza Tooba Aftab, Abdul Rehman, Mohsen Ali, Waqas Sultani
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