用深度学习提升白血病分型准确率，接近90%。
Breaking Down the Hierarchy: A New Approach to Leukemia Classification
- 构建层级标签体系，模仿临床诊断流程。
- 在多个亚型上达到约90%分类准确率。
- 结合CNN与ViT，提升模型可解释性。
白血病是一种复杂的多因素癌症，影响白血球，诊断和治疗面临巨大挑战，主要源于依赖耗时的形态学分析和专家判断，易出错。本研究提出一种改进的综合策略，利用先进的深度学习技术对白血病亚型进行分类。首先构建层级标签体系，为区分不同亚型奠定基础。研究进一步提出一种受临床流程启发的新型层级分类方法，可准确识别多种白血病类型以及反应性与健康细胞。本研究还系统评估了卷积神经网络（CNN）与视觉变换器（ViT）作为分类器的表现。实验结果表明，所提方法在所有白血病亚型上实现约90%的准确率。通过可视化展示实验结果，增强模型可解释性，有助于理解分类过程。
原文摘要 · Abstract (English)
The complexities inherent to leukemia, multifaceted cancer affecting white blood cells, pose considerable diagnostic and treatment challenges, primarily due to reliance on laborious morphological analyses and expert judgment that are susceptible to errors. Addressing these challenges, this study presents a refined, comprehensive strategy leveraging advanced deep-learning techniques for the classification of leukemia subtypes. We commence by developing a hierarchical label taxonomy, paving the way for differentiating between various subtypes of leukemia. The research further introduces a novel hierarchical approach inspired by clinical procedures capable of accurately classifying diverse types of leukemia alongside reactive and healthy cells. An integral part of this study involves a meticulous examination of the performance of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) as classifiers. The proposed method exhibits an impressive success rate, achieving approximately 90\% accuracy across all leukemia subtypes, as substantiated by our experimental results. A visual representation of the experimental findings is provided to enhance the model's explainability and aid in understanding the classification process.
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