Comparison of unenhanced magnetic resonance imaging and ultrasound in detecting very small hepatocellular carcinoma

BACKGROUND In hepatocellular carcinoma(HCC),detection and treatment prior to growth beyond 2 cm are important as a larger tumor size is more frequently associated with microvascular invasion and/or satellites.In the surveillance of very small HCC nodules(≤2 cm in maximum diameter,Barcelona clinical stage 0),we demonstrated that the tumor markers alpha-fetoprotein and PIVKA-Ⅱare not so useful.Therefore,we must survey with imaging modalities.The superiority of magnetic resonance imaging(MRI)over ultrasound(US)to detect HCC was confirmed in many studies.Although enhanced MRI is now performed to accurately diagnose HCC,in conventional clinical practice for HCC surveillance in liver diseases,unenhanced MRI is widely performed throughout the world.While,MRI has made marked improvements in recent years.AIM To make a comparison of unenhanced MRI and US in detecting very small HCC that was examined in the last ten years in patients in whom MRI and US examinations were performed nearly simultaneously.METHODS In 394 patients with very small HCC nodules,those who underwent MRI and US at nearly the same time(on the same day whenever possible or at least within 14 days of one another)at the first diagnosis of HCC were selected.The detection rate of HCC with unenhanced MRI was investigated and compared with that of unenhanced US.RESULTS The sensitivity of unenhanced MRI for detecting very small HCC was 95.1%(97/102,95%confidence interval:90.9-99.3)and that of unenhanced US was 69.6%(71/102,95%confidence interval:60.7-78.5).The sensitivity of unenhanced MRI for detecting very small HCC was significantly higher than that of unenhanced US(P<0.001).Regarding the location of HCC in the liver in patients in whom detection by US was unsuccessful,S7-8 was identified in 51.7%.CONCLUSION Currently,unenhanced MRI is a very useful tool for the surveillance of very small HCC in conventional clinical follow-up practice.

An Intelligent Recommendation System for Real Estate Commodity

Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms,whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search processrenders it difficult for agents and consumers to understand the status changes ofobjects. In this study, Python is used to write web crawler and image recognitionprograms to capture object information from the web pages of real estate agents;perform data screening, arranging, and cleaning;compare the text of real estateobject information;as well as integrate and use the convolutional neural networkof a deep learning algorithm to implement image recognition. In this study, dataare acquired from two business-to-consumer real estate agency networks, i.e., theSinyi real estate agent and the Yungching real estate agent, and one consumer-toconsumer real estate agency platform, i.e., the, FiveNineOne real estate agent. Theresults indicate that text mining can reveal the similarities and differences betweenthe objects, list the number of days that the object has been available for sale onthe website, and provide the price fluctuations and fluctuation times during thesales period. In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%. The dynamic recommendation system for realestate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differencesbetween their commodities and other businesses in approximately 2 min, as wellas rapidly determine developable objects via comparison results provided by thesystem. Meanwhile, consumers require less time in searching and comparingprices after they have understood the commodity dynamic information, therebyallowing them to use the most efficient approach to purchase real estate objectsof their interest.

An Approach to Checking 3D Model with Related Engineering Drawings

For some reasons, engineers build their product 3D mo del according to a set of related engineering drawings. The problem is how we ca n know the 3D model is correct. The manual checking is very boring and time cons uming, and still could not avoid mistakes. Thus, we could not confirm the model, maybe try checking again. It will effect the production preparing cycle greatly , and should be solved in a intelligent way. The difficulties are quite obvious, unlike word checking in a word processing package, the checking described above is not a comparison between same items. One is 2D drawing, the another is 3D mo del, they are not in the same dimension. So, we should make a change for compari son in the same dimension. If we can rebuild a 3D model through related 2D drawi ngs automatically, that’s great. We can not only compare two 3D models to check and correct, but also omit the manual process itself completely. Unfortunately, we can not build such a 3D model automatically right now. So only one way left: compare two 2D drawings, one is the original, the another is processed from tha t manual built one.The method is to select a drawing as a background, rotate th e 3D model and make projections, compare projection with the background automati cally to find a case which they meet each other in certain amount of error ( tolerance), otherwise alarm. This process can be repeated many times if needed t o fulfil the checking task. Also, this is a man-machine system, computer does h ard working, man keeps final decision. The project involved in CAD, VRML, patter n recognition, image capture and comparison, artificial intelligence.

Versus—A tool for evaluating visualizations and image quality using a 2AFC methodology

Novel visualization methods and strategies are necessary to cope with the deluge of datasets present in any scientific field to make discoveries and find answers to previously unanswered questions.These methods and strategies should not only present scientific findings as images in a concise way but also need to be effective and expressive,which often remain untested.Here,we present Versus,a tool to enable easy image quality assessment and image ranking,utilizing a two-alternative forced choice methodology(2AFC)and an efficient ranking algorithm based on a binary search.The tool provides a systematic way of setting up evaluation experiments via the web without the necessity to install any additional software or require any programming skills.Furthermore,Versus can easily interface with crowdsourcing platforms,such as Amazon’s Mechanical Turk,or can be used as a stand-alone system to carry out evaluations with experts.We demonstrate the use of Versus by means of an image evaluation study,aiming to determine if hue,saturation,brightness,and texture are good indicators of uncertainty in three-dimensional protein structures.Drawing from the power of crowdsourcing,we argue that there is demand and also great potential for this tool to become a standard for simple and fast image evaluations,with the aim to test the effectiveness and expressiveness of scientific visualizations.