mlsanityData to 7 Oct 2026

BROW: Better featuRes fOr Whole slide image based on self-distillation

arXiv 2309.08259 ↗First posted 15 Sep 2023cs.CVcited by 4

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Abstract (arXiv)

Whole slide image (WSI) processing is becoming part of the key components of standard clinical diagnosis for various diseases. However, the direct application of conventional image processing algorithms to WSI faces certain obstacles because of WSIs' distinct property: the super-high resolution. The performance of most WSI-related tasks relies on the efficacy of the backbone which extracts WSI patch feature representations. Hence, we proposed BROW, a foundation model for extracting better feature representations for WSIs, which can be conveniently adapted to downstream tasks without or with slight fine-tuning. The model takes transformer architecture, pretrained using self-distillation framework. To improve model's robustness, techniques such as patch shuffling have been employed. Additionally, the model leverages the unique properties of WSIs, utilizing WSI's multi-scale pyramid to incorporate an additional global view, thereby further enhancing its performance. We used both private and public data to make up a large pretraining dataset, containing more than 11000 slides, over 180M extracted patches, encompassing WSIs related to various organs and tissues. To assess the effectiveness of ourmodel, we run a wide range of downstream tasks, including slide-level subtyping, patch-level classification and nuclei instance segmentation. The results confirmed the efficacy, robustness and good generalization ability of the proposed model. This substantiates its potential as foundation model for WSI feature extraction and highlights promising prospects for its application in WSI processing.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 35 of its 62 references that are in the map. First posted on arXiv 15 Sep 2023.

Builds on

35 of 62 references.

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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  2. 2025Foundation Models in Medical Imaging: A Review and Outlookcited by 7
  3. 2024OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicinecited by 5
  4. 2024EFCM: Efficient Fine-tuning on Compressed Models for deployment of large models in medical image analysiscited by 0