Multilingual Hematology Visual Question Answering Dataset

2026-06-23

AI Summary

This study addresses the scarcity of multilingual visual-language resources in hematology, which hinders effective patient communication and clinical documentation in linguistically diverse settings such as Pakistan, where patients’ spoken language often differs from that of medical texts. To bridge this gap, the authors introduce WBCMor VQA, the first large-scale morphology-aware visual question answering benchmark comprising 110K English-Urdu bilingual question-answer pairs grounded in 20K images of leukemic and normal white blood cells. Built upon fine-grained morphological annotations from LeukemiaAttri and WBCAtt, the dataset integrates medical language processing with visual reasoning and is accompanied by a domain-specific Urdu hematological terminology lexicon. Evaluation with multiple open-source vision-language models demonstrates its efficacy, establishing a foundational resource for developing multilingual medical AI systems.

Abstract

Vision Language Models (VLMs) have shown promising capabilities in medical image analysis by jointly understanding visual and textual information for tasks such as Visual Question Answering. However, existing hematology vision-language resources remain predominantly English centric, limiting their applicability in multilingual healthcare environments. This challenge is releveant generally to South Asia and specifically to Pakistan, where Urdu is widely used despite healthcare information and digital medical systems being largely dependent on English. To investigate this gap, we conducted a survey among healthcare professionals, which revealed substantial language mismatches between clinical documentation and patient communication, emphasizing the need for multilingual healthcare technologies. To address this limitation, we introduce WBCMor VQA, a clinically validated bilingual English, Urdu morphology aware VQA benchmark for leukemia and normal white blood cell analysis. The benchmark is constructed using morphology-aware annotations from LeukemiaAttri and WBCAtt datasets and supported by a domain specific Urdu hematology dictionary to ensure linguistic consistency and clinical correctness. The final benchmark contains 110K bilingual question answer pairs serving as VQA annotations for 20K leukemic and normal single-cell images. Furthermore, we establish baseline performance by evaluating multiple open-source VLMs on the proposed benchmark. The proposed resource aims to facilitate the development of accessible and clinically relevant AI systems for multilingual healthcare environments.

Problem

• Multilingual
• Hematology
• Visual Question Answering
• Language Mismatch
• Healthcare

Innovation

• Multilingual VQA
• Hematology
• Vision-Language Models
• Urdu Medical NLP
• Morphology-aware Annotation