Determining lymphoma subtypes is a crucial step for better patient treatment targeting to potentially increase their survival chances. In this context, the existing gold standard diagnosis method, which relies on gene expression technology, is highly expensive and time-consuming, making it less accessibility. Although alternative diagnosis methods based on IHC (immunohistochemistry) technologies exist (recommended by the WHO), they still suffer from similar limitations and are less accurate. Whole Slide Image (WSI) analysis using deep learning models has shown promising potential for cancer diagnosis, that could offer cost-effective and faster alternatives to existing methods. In this work, we propose a vision transformer-based framework for distinguishing DLBCL (Diffuse Large B-Cell Lymphoma) cancer subtypes from high-resolution WSIs. To this end, we introduce a multi-modal architecture to train a classifier model from various WSI modalities. We then leverage this model through a knowledge distillation process to efficiently guide the learning of a mono-modal classifier. Our experimental study conducted on a lymphoma dataset of 157 patients shows the promising performance of our mono-modal classification model, outperforming six recent state-of-the-art methods. In addition, the power-law curve, estimated on our experimental data, suggests that with more training data from a reasonable number of additional patients, our model could achieve competitive diagnosis accuracy with IHC technologies. Furthermore, the efficiency of our framework is confirmed through an additional experimental study on an external breast cancer dataset (BCI dataset).

翻译：确定淋巴瘤亚型是为患者提供精准治疗、提升其生存机会的关键步骤。当前依赖基因表达技术的金标准诊断方法成本高昂且耗时漫长，可及性较低。尽管存在基于免疫组织化学技术的替代诊断方案（获世界卫生组织推荐），这些方法仍面临类似的局限性且准确性不足。利用深度学习模型进行全切片图像分析已展现出在癌症诊断中的巨大潜力，有望为现有方法提供更具成本效益且更快速的替代方案。本研究提出一种基于视觉Transformer的框架，用于从高分辨率全切片图像中区分弥漫性大B细胞淋巴瘤亚型。为此，我们引入多模态架构以从不同全切片图像模态中训练分类器模型，继而通过知识蒸馏过程有效指导单模态分类器的学习。在包含157名患者的淋巴瘤数据集上的实验表明，我们的单模态分类模型性能优异，超越了六种当前最先进的方法。此外，基于实验数据拟合的幂律曲线显示，通过获取合理数量新增患者的训练数据，我们的模型有望达到与免疫组织化学技术相当的诊断准确率。最后，在外部乳腺癌数据集上的补充实验进一步验证了本框架的有效性。