Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis
This addresses a bottleneck in histopathology WSI analysis for medical imaging, offering an incremental improvement in efficiency and stability.
The paper tackled the problem of limited data augmentation in gigapixel whole slide image analysis due to fixed patch representations, proposing a promptable representation distribution learning framework that stably outperforms state-of-the-art methods.
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.