Recent studies have made remarkable progress in histopathology
classification. Based on current successes, contemporary works proposed to
further upgrade the model towards a more generalizable and robust direction
through incrementally learning from the sequentially delivered domains. Unlike
previous parameter isolation based approaches that usually demand massive
computation resources during model updating, we present a memory-efficient
prompt tuning framework to cultivate model generalization potential in
economical memory cost. For each incoming domain, we reuse the existing
parameters of the initial classification model and attach lightweight trainable
prompts into it for customized tuning. Considering the domain heterogeneity, we
perform decoupled prompt tuning, where we adopt a domain-specific prompt for
each domain to independently investigate its distinctive characteristics, and
one domain-invariant prompt shared across all domains to continually explore
the common content embedding throughout time. All domain-specific prompts will
be appended to the prompt bank and isolated from further changes to prevent
forgetting the distinctive features of early-seen domains. While the
domain-invariant prompt will be passed on and iteratively evolve by
style-augmented prompt refining to improve model generalization capability over
time. In specific, we construct a graph with existing prompts and build a
style-augmented graph attention network to guide the domain-invariant prompt
exploring the overlapped latent embedding among all delivered domains for more
domain generic representations. We have extensively evaluated our framework
with two histopathology tasks, i.e., breast cancer metastasis classification
and epithelium-stroma tissue classification, where our approach yielded
superior performance and memory efficiency over the competing methods.

近期的研究在组织病理学分类方面取得了显著进展，提出了一种基于内存高效的提示调优框架，通过使用领域特定和领域不变的提示来提高模型的泛化能力，并且在乳腺癌转移分类和上皮结缔组织分类等任务上表现出卓越的性能和内存效率。

内存高效的增量组织病理学分类提示调节

Memory-Efficient Prompt Tuning for Incremental Histopathology  Classification

Histologic examination plays a crucial role in oncology research and
diagnostics. The adoption of digital scanning of whole slide images (WSI) has
created an opportunity to leverage deep learning-based image classification
methods to enhance diagnosis and risk stratification. Technical limitations of
current approaches to training deep convolutional neural networks (DCNN) result
in suboptimal model performance and make training and deployment of
comprehensive classification models unobtainable. In this study, we introduce a
novel approach that addresses the main limitations of traditional
histopathology classification model training. Our method, termed Learned
Resizing with Efficient Training (LRET), couples efficient training techniques
with image resizing to facilitate seamless integration of larger histology
image patches into state-of-the-art classification models while preserving
important structural information.
We used the LRET method coupled with two distinct resizing techniques to
train three diverse histology image datasets using multiple diverse DCNN
architectures. Our findings demonstrate a significant enhancement in
classification performance and training efficiency. Across the spectrum of
experiments, LRET consistently outperforms existing methods, yielding a
substantial improvement of 15-28% in accuracy for a large-scale, multiclass
tumor classification task consisting of 74 distinct brain tumor types. LRET not
only elevates classification accuracy but also substantially reduces training
times, unlocking the potential for faster model development and iteration. The
implications of this work extend to broader applications within medical imaging
and beyond, where efficient integration of high-resolution images into deep
learning pipelines is paramount for driving advancements in research and
clinical practice.

通过采用 Learned Resizing with Efficient Training (LRET) 方法，结合了高效训练技术和图像调整，以提高组织病理学分类模型的性能和训练效率，同时保留重要的结构信息，从而在肿瘤分类任务中显著提高分类性能和减少训练时间。

带有高效训练的学习图像调整（LRET）改进大规模数字组织病理学图像分类模型的性能

Learned Image resizing with efficient training (LRET) facilitates  improved performance of large-scale digital histopathology image  classification models