Histopathological images are the gold standard for diagnosing liver cancer.
However, the accuracy of fully digital diagnosis in computational pathology
needs to be improved. In this paper, in order to solve the problem of
multi-label and low classification accuracy of histopathology images, we
propose a locally deep convolutional Swim framework (LDCSF) to classify
multi-label histopathology images. In order to be able to provide local field
of view diagnostic results, we propose the LDCSF model, which consists of a
Swin transformer module, a local depth convolution (LDC) module, a feature
reconstruction (FR) module, and a ResNet module. The Swin transformer module
reduces the amount of computation generated by the attention mechanism by
limiting the attention to each window. The LDC then reconstructs the attention
map and performs convolution operations in multiple channels, passing the
resulting feature map to the next layer. The FR module uses the corresponding
weight coefficient vectors obtained from the channels to dot product with the
original feature map vector matrix to generate representative feature maps.
Finally, the residual network undertakes the final classification task. As a
result, the classification accuracy of LDCSF for interstitial area, necrosis,
non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively.
Finally, we use the results of multi-label pathological image classification to
calculate the tumor-to-stromal ratio, which lays the foundation for the
analysis of the microenvironment of liver cancer histopathological images.
Second, we released a multilabel histopathology image of liver cancer, our code
and data are available at this https URL

为了提高组织病理图像的多标签分类准确性，本文提出了一种局部深度卷积浮游 (Swin) 框架 (LDCSF) 来分类多标签组织病理图像，并基于该分类结果计算了肝癌组织病理图像的肿瘤至基质比率。

LDCSF: 基于局部深度卷积的水生框架用于多标签组织病理学图像分类

LDCSF: Local depth convolution-based Swim framework for classifying  multi-label histopathology images

Contrastive learning has gained popularity due to its robustness with good
feature representation performance. However, cosine distance, the commonly used
similarity metric in contrastive learning, is not well suited to represent the
distance between two data points, especially on a nonlinear feature manifold.
Inspired by manifold learning, we propose a novel extension of contrastive
learning that leverages geodesic distance between features as a similarity
metric for histopathology whole slide image classification. To reduce the
computational overhead in manifold learning, we propose geodesic-distance-based
feature clustering for efficient contrastive loss evaluation using prototypes
without time-consuming pairwise feature similarity comparison. The efficacy of
the proposed method is evaluated on two real-world histopathology image
datasets. Results demonstrate that our method outperforms state-of-the-art
cosine-distance-based contrastive learning methods.

提出了一种基于测地线距离的对比学习方法，通过特征聚类减少计算成本，用于组织病理学切片图像分类，结果表明我们的方法优于基于余弦距离的现有方法。

使用深度流形对比学习的组织病理学图像分类

Histopathology Image Classification using Deep Manifold Contrastive  Learning

Applying curriculum learning requires both a range of difficulty in data and
a method for determining the difficulty of examples. In many tasks, however,
satisfying these requirements can be a formidable challenge. In this paper, we
contend that histopathology image classification is a compelling use case for
curriculum learning. Based on the nature of histopathology images, a range of
difficulty inherently exists among examples, and, since medical datasets are
often labeled by multiple annotators, annotator agreement can be used as a
natural proxy for the difficulty of a given example. Hence, we propose a simple
curriculum learning method that trains on progressively-harder images as
determined by annotator agreement. We evaluate our hypothesis on the
challenging and clinically-important task of colorectal polyp classification.
Whereas vanilla training achieves an AUC of 83.7% for this task, a model
trained with our proposed curriculum learning approach achieves an AUC of
88.2%, an improvement of 4.5%. Our work aims to inspire researchers to think
more creatively and rigorously when choosing contexts for applying curriculum
learning.

该论文提出了一种简单的课程学习方法，用于根据标注者一致性评估的难度逐步训练组织病理学图像分类模型，在结直肠息肉分类任务中将模型性能从 83.7% 的 AUC 提高到 88.2% 的 AUC，以启发研究人员在选择应用课程学习时更具创造性和严谨性。