Multiple instance learning (MIL) has emerged as a popular method for
classifying histopathology whole slide images (WSIs). Existing approaches
typically rely on frozen pre-trained models to extract instance features,
neglecting the substantial domain shift between pre-training natural and
histopathological images. To address this issue, we propose PAMT, a novel
Prompt-guided Adaptive Model Transformation framework that enhances MIL
classification performance by seamlessly adapting pre-trained models to the
specific characteristics of histopathology data. To capture the intricate
histopathology distribution, we introduce Representative Patch Sampling (RPS)
and Prototypical Visual Prompt (PVP) to reform the input data, building a
compact while informative representation. Furthermore, to narrow the domain
gap, we introduce Adaptive Model Transformation (AMT) that integrates adapter
blocks within the feature extraction pipeline, enabling the pre-trained models
to learn domain-specific features. We rigorously evaluate our approach on two
publicly available datasets, Camelyon16 and TCGA-NSCLC, showcasing substantial
improvements across various MIL models. Our findings affirm the potential of
PAMT to set a new benchmark in WSI classification, underscoring the value of a
targeted reprogramming approach.

多实例学习（MIL）作为一种流行的方法正在用于对组织病理学全幅切片图像进行分类。作者提出了 PAMT，这是一种新颖的基于提示的自适应模型转换框架，通过无缝地将预训练模型适应组织病理学数据的特点来提高 MIL 分类性能。作者引入了代表性补丁采样（RPS）和典型视觉提示（PVP）来重构输入数据，构建一个紧凑而且信息丰富的表示。此外，为了缩小域差异，作者在特征提取流水线中引入了适应性模型转换（AMT），使预训练模型能够学习特定领域的特征。作者在两个公开数据集 Camelyon16 和 TCGA-NSCLC 上对该方法进行了严格评估，展示了各种 MIL 模型的显著改进。作者的发现证实了 PAMT 在 WSI 分类中的潜力，强调了有针对性的重新编程方法的价值。

基于提示的自适应模型转换用于大规模图像分类

Prompt-Guided Adaptive Model Transformation for Whole Slide Image  Classification

Getting good performance out of numerical equation solvers requires that the
user has provided stable and efficient functions representing their model.
However, users should not be trusted to write good code. In this manuscript we
describe ModelingToolkit (MTK), a symbolic equation-based modeling system which
allows for composable transformations to generate stable, efficient, and
parallelized model implementations. MTK blurs the lines of traditional symbolic
computing by acting directly on a user's numerical code. We show the ability to
apply graph algorithms for automatically parallelizing and performing index
reduction on code written for differential-algebraic equation (DAE) solvers,
"fixing" the performance and stability of the model without requiring any
changes to on the user's part. We demonstrate how composable model
transformations can be combined with automated data-driven surrogate generation
techniques, allowing machine learning methods to generate accelerated
approximate models within an acausal modeling framework. These reduced models
are shown to outperform the Dymola Modelica compiler on an HVAC model by 590x
at 3\% error. Together, this demonstrates MTK as a system for bringing the
latest research in graph transformations directly to modeling applications.

这篇论文介绍了 ModelingToolkit (MTK) 这一符号方程为基础的建模工具，该工具可以通过组合变换来生成稳定、高效和并行的模型实现。MTK 通过对用户的数值代码进行处理来提高性能和稳定性，并演示了如何将机器学习方法应用于减少模型复杂性并加速近似建模。