Pathology is the study of microscopic inspection of tissue, and a pathology
diagnosis is often the medical gold standard to diagnose disease. Pathology
images provide a unique challenge for computer-vision-based analysis: a single
pathology Whole Slide Image (WSI) is gigapixel-sized and often contains
hundreds of thousands to millions of objects of interest across multiple
resolutions. In this work, we propose PathoLogy Universal TransfOrmer (PLUTO):
a light-weight pathology FM that is pre-trained on a diverse dataset of 195
million image tiles collected from multiple sites and extracts meaningful
representations across multiple WSI scales that enable a large variety of
downstream pathology tasks. In particular, we design task-specific adaptation
heads that utilize PLUTO's output embeddings for tasks which span pathology
scales ranging from subcellular to slide-scale, including instance
segmentation, tile classification, and slide-level prediction. We compare
PLUTO's performance to other state-of-the-art methods on a diverse set of
external and internal benchmarks covering multiple biologically relevant tasks,
tissue types, resolutions, stains, and scanners. We find that PLUTO matches or
outperforms existing task-specific baselines and pathology-specific foundation
models, some of which use orders-of-magnitude larger datasets and model sizes
when compared to PLUTO. Our findings present a path towards a universal
embedding to power pathology image analysis, and motivate further exploration
around pathology foundation models in terms of data diversity, architectural
improvements, sample efficiency, and practical deployability in real-world
applications.

该研究提出了一种轻量级的病理学 FM，即 PLUTO，它在多个病理学尺度上从多个 WSI 中抽取有意义的表示，以支持多样化的病理学任务，包括实例分割、瓦片分类和幻灯片级别预测，并通过在多个基于生物的任务、组织类型、分辨率、染色和扫描仪的外部和内部基准测试中的表现，证明 PLUTO 相当于或优于现有的基线和基础模型。该研究为病理图像分析提供了通用嵌入的发展方向，并促进进一步探索病理学基础模型在数据多样性、架构改进、样本效率和实际应用中的可行性方面。

PLUTO：病理通用 Transformer

PLUTO: Pathology-Universal Transformer

Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual
Prompt Tuning (VPT) have found success in enabling adaptation to new domains by
tuning small modules within a transformer model. However, the number of domains
encountered during test time can be very large, and the data is usually
unlabeled. Thus, adaptation to new domains is challenging; it is also
impractical to generate customized tuned modules for each such domain. Toward
addressing these challenges, this work introduces PLUTO: a Plug-and-pLay
modUlar Test-time domain adaptatiOn strategy. We pre-train a large set of
modules, each specialized for different source domains, effectively creating a
``module store''. Given a target domain with few-shot unlabeled data, we
introduce an unsupervised test-time adaptation (TTA) method to (1) select a
sparse subset of relevant modules from this store and (2) create a weighted
combination of selected modules without tuning their weights. This
plug-and-play nature enables us to harness multiple most-relevant source
domains in a single inference call. Comprehensive evaluations demonstrate that
PLUTO uniformly outperforms alternative TTA methods and that selecting $\leq$5
modules suffice to extract most of the benefit. At a high level, our method
equips pre-trained transformers with the capability to dynamically adapt to new
domains, motivating a new paradigm for efficient and scalable domain
adaptation.

通过引入 PLUTO（Plug-and-pLay modUlar Test-time domain adaptatiOn）方法，本研究介绍了一种预训练模型通过无监督的测试时自适应方法来选择和组合相关模块，实现了动态适应新领域的能力，从而对于参数高效的调整、领域适应提出了一种新的有效和可伸缩的范式。