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<p>Accurate classification of leukemia subtypes (AML, ALL, CLL, CML) from descriptive text and cell images requires multimodal approaches that align visual and textual information. We developed a novel framework integrating Vision-and-Language Transformer (VILT) with Multi-Domain Feature Aggregation (MDFA) methodology for text-guided leukemia classification. The architecture incorporates Consistency-Aware Cycle GAN to synthesize balanced text-image combinations and mitigate class distribution disparities, while leveraging hierarchical feature extraction for enhanced semantic-visual correspondence. Experimental validation on the Raabin hematological dataset achieved 76.8% classification accuracy, outperforming contemporary medical vision-language architectures including MedCLIP-SAM (73.8%) and BiomedGPT-V (72.1%). This work establishes new benchmarks for text-driven hematological malignancy analysis and provides a reproducible methodology for textual-to-leukemia-type correlation applications in clinical diagnostics.</p>

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Multimodal vision-language framework for text-guided leukemia classification using advanced deep learning architectures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月19日

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  • Mohammad Momenian,
  • Seyed Vahab Shojaedini

摘要

Accurate classification of leukemia subtypes (AML, ALL, CLL, CML) from descriptive text and cell images requires multimodal approaches that align visual and textual information. We developed a novel framework integrating Vision-and-Language Transformer (VILT) with Multi-Domain Feature Aggregation (MDFA) methodology for text-guided leukemia classification. The architecture incorporates Consistency-Aware Cycle GAN to synthesize balanced text-image combinations and mitigate class distribution disparities, while leveraging hierarchical feature extraction for enhanced semantic-visual correspondence. Experimental validation on the Raabin hematological dataset achieved 76.8% classification accuracy, outperforming contemporary medical vision-language architectures including MedCLIP-SAM (73.8%) and BiomedGPT-V (72.1%). This work establishes new benchmarks for text-driven hematological malignancy analysis and provides a reproducible methodology for textual-to-leukemia-type correlation applications in clinical diagnostics.