A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction,segmentation, classification and detection approaches

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted.

     

Aqueous-based,high-density nanoporous carbon xerogels with high specific surface area for supercapacitors

Journal of Porous Materials - Electrode materials with high density for assembling supercapacitors with high volumetric capacitance are urgently needed. Herein, nanoporous carbon xerogels (NPCXs)...     

偏铁酸钠催化合成共轭亚油酸

以红花菜籽油中亚油酸为原料,偏铁酸钠为催化剂,在单因素试验的基础上,选取反应温度、反应时间、催化剂用量为考察因素,以异构化产物共轭亚油酸(CLA)转化率为响应值,通过响应面分析法对异构化反应进行优化,并建立相应的回归模型,试验结果表明,红花菜籽油中亚油酸异构化为CLA的最佳工艺为:反应温度195℃、反应时间4.7 h、催化剂用量0.6 g,在此条件下得到CLA转化率为92.90％,与预测值93.92％相比,误差为1.02％.然后再通过脲素包合纯化、经2％的H2SO4-CH3OH溶液甲酯化后,用气相色谱-质谱联用仪检测CLA含量为64.27％.     

Using refined kano model and decision trees to discover learners’ needs for teaching videos

Multimedia Tools and Applications - With the advancement of technology and the spread of the COVID19 epidemic, learning can no longer only be done through face-to-face teaching. Numerous digital...     

Modeling and Verification of a Sentiment Analysis System Using Aspect-Oriented Petri Nets

An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets (AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.