Digital pathology and the integration of artificial intelligence (AI) models
have revolutionized histopathology, opening new opportunities. With the
increasing availability of Whole Slide Images (WSIs), there's a growing demand
for efficient retrieval, processing, and analysis of relevant images from vast
biomedical archives. However, processing WSIs presents challenges due to their
large size and content complexity. Full computer digestion of WSIs is
impractical, and processing all patches individually is prohibitively
expensive. In this paper, we propose an unsupervised patching algorithm,
Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This
novel approach condenses a histopathology WSI into a compact set of
representative patches, forming a "collage" of WSI while minimizing redundancy.
SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI
and selecting non-redundant representative features. We evaluated SPLICE for
search and match applications, demonstrating improved accuracy, reduced
computation time, and storage requirements compared to existing
state-of-the-art methods. As an unsupervised method, SPLICE effectively reduces
storage requirements for representing tissue images by 50%. This reduction
enables numerous algorithms in computational pathology to operate much more
efficiently, paving the way for accelerated adoption of digital pathology.

数字病理学和人工智能模型的整合已经改变了组织病理学，为全面扫描图像（WSIs）的处理和分析提供了新机遇。本文提出了一种无监督补丁算法 SPLICE，通过顺序分析 WSIs 并选择非冗余特征，将 WSIs 压缩为紧凑的代表性补丁集，从而提高了搜索和匹配应用的准确性，减少了计算时间和存储需求。SPLICE 作为一种无监督方法，可以将组织图像的存储需求降低 50％，从而使计算病理学的许多算法能够更高效地运行，为数字病理学的快速应用铺平了道路。

简化数字病理图像处理的 SPLICE 方法

SPLICE -- Streamlining Digital Pathology Image Processing

We present a method for semantically transferring the visual appearance of
one natural image to another. Specifically, our goal is to generate an image in
which objects in a source structure image are "painted" with the visual
appearance of their semantically related objects in a target appearance image.
To integrate semantic information into our framework, our key idea is to
leverage a pre-trained and fixed Vision Transformer (ViT) model. Specifically,
we derive novel disentangled representations of structure and appearance
extracted from deep ViT features. We then establish an objective function that
splices the desired structure and appearance representations, interweaving them
together in the space of ViT features. Based on our objective function, we
propose two frameworks of semantic appearance transfer -- "Splice", which works
by training a generator on a single and arbitrary pair of structure-appearance
images, and "SpliceNet", a feed-forward real-time appearance transfer model
trained on a dataset of images from a specific domain. Our frameworks do not
involve adversarial training, nor do they require any additional input
information such as semantic segmentation or correspondences. We demonstrate
high-resolution results on a variety of in-the-wild image pairs, under
significant variations in the number of objects, pose, and appearance. Code and
supplementary material are available in our project page: splice-vit.github.io.

我们提出了一种语义外观传输的方法，通过利用预先训练的和固定的视觉 Transformer（ViT）模型，提取深度 ViT 特征中结构和外观的新型解耦表示，然后通过 ViT 特征空间中的目标函数将这些表示相互编织在一起，提出了两种语义外观传输的框架 ——Splice 和 SpliceNet。

在 ViT 特征空间中解耦结构和外观

Disentangling Structure and Appearance in ViT Feature Space

We present a method for semantically transferring the visual appearance of
one natural image to another. Specifically, our goal is to generate an image in
which objects in a source structure image are "painted" with the visual
appearance of their semantically related objects in a target appearance image.
Our method works by training a generator given only a single
structure/appearance image pair as input. To integrate semantic information
into our framework - a pivotal component in tackling this task - our key idea
is to leverage a pre-trained and fixed Vision Transformer (ViT) model which
serves as an external semantic prior. Specifically, we derive novel
representations of structure and appearance extracted from deep ViT features,
untwisting them from the learned self-attention modules. We then establish an
objective function that splices the desired structure and appearance
representations, interweaving them together in the space of ViT features. Our
framework, which we term "Splice", does not involve adversarial training, nor
does it require any additional input information such as semantic segmentation
or correspondences, and can generate high-resolution results, e.g., work in HD.
We demonstrate high quality results on a variety of in-the-wild image pairs,
under significant variations in the number of objects, their pose and
appearance.

本文介绍了一种名为 Splice 的方法，通过集成 ViT 模型的语义信息，将一个自然图像中的物体的视觉外观转移到另一个图像中，该方法不需要使用对抗训练，也不需要任何额外的输入信息。

利用 ViT 特征进行语义外观转换

Splicing ViT Features for Semantic Appearance Transfer

Given n observations of a p-dimensional random vector, the covariance matrix
and its inverse (precision matrix) are needed in a wide range of applications.
Sample covariance (e.g. its eigenstructure) can misbehave when p is comparable
to the sample size n. Regularization is often used to mitigate the problem.
In this paper, we proposed an l1-norm penalized pseudo-likelihood estimate
for the inverse covariance matrix. This estimate is sparse due to the l1-norm
penalty, and we term this method SPLICE. Its regularization path can be
computed via an algorithm based on the homotopy/LARS-Lasso algorithm.
Simulation studies are carried out for various inverse covariance structures
for p=15 and n=20, 1000. We compare SPLICE with the l1-norm penalized
likelihood estimate and a l1-norm penalized Cholesky decomposition based
method. SPLICE gives the best overall performance in terms of three metrics on
the precision matrix and ROC curve for model selection. Moreover, our
simulation results demonstrate that the SPLICE estimates are positive-definite
for most of the regularization path even though the restriction is not
enforced.

本研究提出了一种基于 l1 范数惩罚的拟似然估计方法来计算协方差矩阵和逆矩阵，称之为 SPLICE 方法。该方法在模型精度和模型选择方面表现出最佳性能。