Multilingual Hematology Visual Question Answering Dataset
On the map: Multimodal LLMs / vision-language models → Medical vision-language models
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 10 of its 20 references that are in the map. First posted on arXiv 24 Jun 2026.
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- 2026HemBLIP: A Vision-Language Model for Interpretable Leukemia Cell Morphology Analysis7 Jan
- 2025Vision Language Models in Medicine24 Feb
- 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review4 Mar
Other references · 7
- 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,191
- 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,546
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,302
- 2024BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domainscited by 299
- 2023WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributescited by 22
- 2025LoRA in LoRA: Towards Parameter-Efficient Architecture Expansion for Continual Visual Instruction Tuningcited by 13
- 2025Uni-Hema: Unified Model for Digital Hematopathologycited by 2
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- 2026How Good LLMs Are at Answering Bangla Medical Visual Questions? Dataset and Benchmarking
- 2026Analyzing and Mitigating Cross-Lingual Degradation in Multilingual Medical VQA
- 2026Does Language Shift Break Medical Vision-Language Models? Indonesian Radiology Visual Question Answering Case Study
- 2024CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
- 2024Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
- 2025PersianMedQA: Evaluating Large Language Models on a Persian-English Bilingual Medical Question Answering Benchmark