Digital pathology has become a standard in the pathology workflow due to its
many benefits. These include the level of detail of the whole slide images
generated and the potential immediate sharing of cases between hospitals.
Recent advances in deep learning-based methods for image analysis make them of
potential aid in digital pathology. However, a major limitation in developing
computer-aided diagnostic systems for pathology is the lack of an intuitive and
open web application for data annotation. This paper proposes a web service
that efficiently provides a tool to visualize and annotate digitized
histological images. In addition, to show and validate the tool, in this paper
we include a use case centered on the diagnosis of spindle cell skin neoplasm
for multiple annotators. A usability study of the tool is also presented,
showing the feasibility of the developed tool.

本文提出了一种网络服务来可视化和注释数字化组织学图像，并通过一个以多个注释器为中心的用例来展示和验证该工具，同时提出了一个可用性研究，证明了该工具的可行性。

HistoColAi：基于人工智能驱动预测整合的数字组织学图像协同注释开源 Web 平台

HistoColAi: An Open-Source Web Platform for Collaborative Digital  Histology Image Annotation with AI-Driven Predictive Integration

There is a strong need for automated systems to improve diagnostic quality
and reduce the analysis time in histopathology image processing. Automated
detection and classification of pathological tissue characteristics with
computer-aided diagnostic systems are a critical step in the early diagnosis
and treatment of diseases. Once a pathology image is scanned by a microscope
and loaded onto a computer, it can be used for automated detection and
classification of diseases. In this study, the DenseNet-161 and ResNet-50
pre-trained CNN models have been used to classify digital histopathology
patches into the corresponding whole slide images via transfer learning
technique. The proposed pre-trained models were tested on grayscale and color
histopathology images. The DenseNet-161 pre-trained model achieved a
classification accuracy of 97.89% using grayscale images and the ResNet-50
model obtained the accuracy of 98.87% for color images. The proposed
pre-trained models outperform state-of-the-art methods in all performance
metrics to classify digital pathology patches into 24 categories.

利用 DenseNet-161 和 ResNet-50 预训练 CNN 模型，通过迁移学习的方法对数字组织病理学补丁进行分类，实验结果表明，这种方法可以自动检测和分类疾病，并在分类效果方面表现出色。