提出可提示的表示分布学习框架，解决超分辨率病理切片数据增强难题。
Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis
- 通过可提示机制动态学习图像块表示分布
- 在不损失语义的前提下实现高效特征空间数据增强
- 适合需要稳定训练的高分辨率病理图像分析场景
超分辨率病理切片（gigapixel WSI）分析通常依赖多实例学习（MIL），其中图像块表征在训练过程中固定以保证效率。然而，表征不变性导致难以进行切片级数据增强，严重限制了下游分析性能。现有增强方法或增加计算开销，或丢失语义信息，难以满足WSI模型训练对效率与稳定性要求。本文提出可提示表示分布学习框架（PRDL），同时实现图像块级表征学习与切片级数据增强。探索使用提示引导特征空间数据增强，实现可提示的模型训练。实验表明，该方法稳定优于现有最先进方法。
原文摘要 · Abstract (English)
Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are extracted and then fixed during the training of the MIL classifiers for efficiency consideration. However, the invariance of representations makes it difficult to perform data augmentation for WSI-level model training, which significantly limits the performance of the downstream WSI analysis. The current data augmentation methods for gigapixel images either introduce additional computational costs or result in a loss of semantic information, which is hard to meet the requirements for efficiency and stability needed for WSI model training. In this paper, we propose a Promptable Representation Distribution Learning framework (PRDL) for both patch-level representation learning and WSI-level data augmentation. Meanwhile, we explore the use of prompts to guide data augmentation in feature space, which achieves promptable data augmentation for training robust WSI-level models. The experimental results have demonstrated that the proposed method stably outperforms state-of-the-art methods.
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