Whole Slide Image (WSI) representation is critical for cancer subtyping, cancer recognition and mutation prediction.Training an end-to-end WSI representation model poses significant challenges, as a standard gigapixel slide can contain tens of thousands of image tiles, making it difficult to compute gradients of all tiles in a single mini-batch due to current GPU limitations. To address this challenge, we propose a method of dynamic residual encoding with slide-level contrastive learning (DRE-SLCL) for end-to-end WSI representation. Our approach utilizes a memory bank to store the features of tiles across all WSIs in the dataset. During training, a mini-batch usually contains multiple WSIs. For each WSI in the batch, a subset of tiles is randomly sampled and their features are computed using a tile encoder. Then, additional tile features from the same WSI are selected from the memory bank. The representation of each individual WSI is generated using a residual encoding technique that incorporates both the sampled features and those retrieved from the memory bank. Finally, the slide-level contrastive loss is computed based on the representations and histopathology reports ofthe WSIs within the mini-batch. Experiments conducted over cancer subtyping, cancer recognition, and mutation prediction tasks proved the effectiveness of the proposed DRE-SLCL method.

翻译：全切片图像（WSI）表示对于癌症亚型分类、癌症识别和突变预测至关重要。训练端到端的WSI表示模型面临重大挑战，因为标准的千兆像素级切片可能包含数万个图像块，在当前GPU限制下难以在单个小批量中计算所有图像块的梯度。为解决这一挑战，我们提出了一种结合滑动窗口级对比学习的动态残差编码方法（DRE-SLCL），用于端到端的WSI表示。我们的方法利用一个记忆库存储数据集中所有WSI的图像块特征。在训练过程中，一个小批量通常包含多个WSI。对于批次中的每个WSI，随机采样一个图像块子集，并使用图像块编码器计算其特征。随后，从记忆库中选取同一WSI的额外图像块特征。每个独立WSI的表示通过残差编码技术生成，该技术整合了采样特征和从记忆库检索的特征。最后，基于小批量内WSI的表示和病理报告计算滑动窗口级对比损失。在癌症亚型分类、癌症识别和突变预测任务上进行的实验证明了所提出的DRE-SLCL方法的有效性。