Determining lymphoma subtypes is a crucial step for better patients treatment
targeting to potentially increase their survival chances. In this context, the
existing gold standard diagnosis method, which is based on gene expression
technology, is highly expensive and time-consuming making difficult its
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. WSI (Whole Slide Image)
analysis by deep learning models showed promising new directions for cancer
diagnosis that would be cheaper and faster than existing alternative 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 propose a multi-modal architecture to
train a classifier model from various WSI modalities. We then exploit this
model through a knowledge distillation mechanism for efficiently driving the
learning of a mono-modal classifier. Our experimental study conducted on a
dataset of 157 patients shows the promising performance of our mono-modal
classification model, outperforming six recent methods from the
state-of-the-art dedicated for cancer classification. Moreover, the power-law
curve, estimated on our experimental data, shows that our classification model
requires a reasonable number of additional patients for its training to
potentially reach identical diagnosis accuracy as IHC technologies.

通过视觉变换器为高分辨率 WSI 从研究多模态分类器训练的 DLBCL（弥漫性大 B 细胞淋巴瘤）癌症亚型提出了一个前景恶劣，比现有的替代方法更便宜，更快速。