论文标题
DSCA：一个双流网络，具有跨注意的全扫描图像金字塔的癌症预后
DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis
论文作者
论文摘要
Gigapixel全裂片图像（WSI）上的癌症预后一直是一项艰巨的任务。为了进一步增强WSI的视觉表示，现有方法在WSIS中探索了图像金字塔，而不是单分辨率图像。尽管如此，它们仍然遇到两个主要问题：高计算成本和多分辨率特征融合中未注意的语义差距。为了解决这些问题，本文提议从新的角度有效利用WSI金字塔，即具有交叉注意的双流网络（DSCA）。我们的关键思想是利用两个子流以两种决议处理WSI贴片，在该分辨率中，在高分辨率流中设计了一个正方形池以显着降低计算成本，并提出了一种基于交叉注意的方法来正确处理双流式特征的融合。我们在三个公共可用数据集中验证了DSCA，总数为1,911名患者，总数为3,101个WSI。我们的实验和消融研究验证了（i）提出的DSCA可以超过癌症预后中现有的最新方法，平均C-指数提高约4.6％； （ii）我们的DSCA网络在计算方面的效率更高 - 与典型的现有多分辨率网络相比，它具有更多可学习的参数（6.31m vs. 860.18k），但计算成本却更少（2.51g vs. 4.94g）。 （iii）DSCA，双流和跨注意事项的关键组成部分确实有助于我们的模型性能，同时保持了相对微小的计算负载，平均C-Index上升约为2.0％。我们的DSCA可以作为基于WSI的癌症预后的替代和有效工具。
The cancer prognosis on gigapixel Whole-Slide Images (WSIs) has always been a challenging task. To further enhance WSI visual representations, existing methods have explored image pyramids, instead of single-resolution images, in WSIs. In spite of this, they still face two major problems: high computational cost and the unnoticed semantical gap in multi-resolution feature fusion. To tackle these problems, this paper proposes to efficiently exploit WSI pyramids from a new perspective, the dual-stream network with cross-attention (DSCA). Our key idea is to utilize two sub-streams to process the WSI patches with two resolutions, where a square pooling is devised in a high-resolution stream to significantly reduce computational costs, and a cross-attention-based method is proposed to properly handle the fusion of dual-stream features. We validate our DSCA on three publicly-available datasets with a total number of 3,101 WSIs from 1,911 patients. Our experiments and ablation studies verify that (i) the proposed DSCA could outperform existing state-of-the-art methods in cancer prognosis, by an average C-Index improvement of around 4.6%; (ii) our DSCA network is more efficient in computation -- it has more learnable parameters (6.31M vs. 860.18K) but less computational costs (2.51G vs. 4.94G), compared to a typical existing multi-resolution network. (iii) the key components of DSCA, dual-stream and cross-attention, indeed contribute to our model's performance, gaining an average C-Index rise of around 2.0% while maintaining a relatively-small computational load. Our DSCA could serve as an alternative and effective tool for WSI-based cancer prognosis.
