面向端到端全切片图像表示的动态残差编码与切片级对比学习
Dynamic Residual Encoding with Slide-Level Contrastive Learning for End-to-End Whole Slide Image Representation
AI总结：
本文提出DRE-SLCL方法，通过记忆库存储图像块特征并结合动态残差编码与切片级对比学习，解决端到端WSI表示中GPU显存限制问题，在癌症亚型分型、识别和突变预测任务上验证了有效性。
摘要：
全切片图像（WSI）表示对于癌症亚型分型、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，因为一张标准的十亿像素级切片可能包含数万个图像块，受当前GPU限制，很难在单个小批量中计算所有图像块的梯度。为应对这一挑战，我们提出了一种用于端到端WSI表示的动态残差编码与切片级对比学习（DRE-SLCL）方法。我们的方法利用一个记忆库来存储数据集中所有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。对于批量中的每个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。然后，从记忆库中选取来自同一WSI的额外图像块特征。每个单独WSI的表示通过一种残差编码技术生成，该技术同时融合了采样得到的特征和从记忆库中检索到的特征。最后，基于小批量内WSI的表示及其组织病理学报告计算切片级对比损失。在癌症亚型分型、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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
