MeCan's Portable Monitor

MeCan's Portable Monitor

2021-06-21
MeCan
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portable monitor is put on the market by Guangzhou MeCan Medical Limited. Its materials are carefully sourced for performance consistency and excellence. Waste and inefficiencies are constantly driven out of every stage of its production; processes are standardized as much as possible; thus this product has achieved world-class standards of quality and cost performance ratio.Customer prefers MeCan products mainly based on good feedback. Customers offer in-depth comments for them, which is of great importance for us to make the improvement. After product upgrades are implemented, the product is bound to attract more customers, making sustainable sales growth possible. A continuous achievement in product sales will help improve the brand image in the market.Minimum order quantity of portable monitor and suchlike products at MeCan has always been the first thing asked by our new customers. It is negotiable and mainly depends on customer's requirements.
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What Exhibitions Do Portable Monitor Manufacturers Attend?
What Exhibitions Do Portable Monitor Manufacturers Attend?
As the world’s largest manufacturing country, China has a wealth of manufacturing companies producing portable monitor. Many of these companies have skilled labor, as well as the excellent engineers required to produce high-quality products. They are able to integrate the expertise, capability, competition, and other factors into the whole running process. Outsourcing manufacturing to these companies have numbers of benefits such as affordable labor costs, high-quality products, a faster production rate, considerate service, a wealth of materials, flexibile manufacturing, etc.MeCan is a leader in the design and manufacture of high-quality portable monitor. By enhancing the performance of portable monitor, the worries of our users can be reduced. Environment of the production base is the fundamental factor for the quality of portable monitor produced by Guangzhou MeCan Medical Limited.Customers value partners who strive to understand and take action to exceed expectations. We can make a difference by serving our customers better than anybody else. Get price!· Related Questions:1. Which portable monitor company doing OBM?MeCan is developing into a major producer of OBM of high quality portable monitor. We'll be accountable for everything, such as development and production, supply chain, delivery, and promotion. In case you have some specifications and samples or pictures, please send us the specifications and we'll create the products depending on your requirements. ——————— 2. Which portable monitor company doing OEM?Almost all portable monitor manufacturers provide OEM service. As a main kind of custom service, OEM is focused on the personalization by material, size, color and other specifications. It is aimed at meeting clients from different industries. This is an effective way to achieve win-win situation between factories and brands. The OEM service providers are hoped to well control the cost, and to ensure that the products could help optimize the client’s brand value. ——————— 3. Any manufacturers to customize portable monitor?The customization of portable monitor is a very common service in the industry. You can find many such enterprises in China. In fact, its customization is a process to maximize the product image and product performance. The Chinese enterprises provide custom service with a clear intention to expand the business. The clients come from different countries and have different use purposes. If the product cannot be customized, the sales would be very limited. In addition, custom service is a way for the domestic manufacturers to have an in-depth sight into the product. This may be an idea when the R&D team is developing new product models. ——————— 4. Reliable company for portable monitorIn this competitive society, MeCan is a reputable portable monitor manufacturer in China. From raw materials selection into a complete product, a reliable supplier should always focus on the perfect and exact operation during each step, ensuring high-quality products are given to customers. The company upholds the tenet that expert support staff is also a very important part of the business. It may guarantee considerate services and timely response.
Any Brands for High End Portable Monitor?
Any Brands for High End Portable Monitor?
Trade shows manufacturers attend are usually targeted in the industry and people involved or interested in this industry. MeCan generally would carry out products and promote testing at commerce shows and exhibitions to acquire business or standard view about our offering, and thus to make better portable monitor. Exhibiting at a trade show can be a great way to advertise.Guangzhou MeCan Medical Limited is a major supplier of portable monitor in China. More convenience for you with our portable monitor. We greatly value every detail when manufacturing portable monitor.We pledge our best efforts to make your experience both beneficial and enjoyable. Get quote!· Related Questions:1. What companies are producing portable monitor?The 21st century has seen the rapid development of portable monitor business. Nowadays there are many enterprises focusing on this field. They are busy establishing a complete supply chain and furthermore completing the whole industry chain. MeCan is one of the players. Since the establishment, we have developed our own technology to simplify the production process and reduce the production cost, and at the same time, we have contracted with several material suppliers to guarantee the property of the finished products. We have a goal to become a leader in the domestic portable monitor segment. ——————— 2. Which portable monitor company doing OEM?As portable monitor is demanding quickly, the needs of customers also vary. Thus, more and more producers start to focus on developing their OEM service. MeCan is one of them. A manufacturer that can do OEM service is capable of manufacturing based on the drawings or sketches provided by the seller. The company has been providing expert OEM service for clients since its establishment. Because of its highly innovative technology and experienced staff, the final product is widely recognized by clients. ——————— 3. Any good brands for portable monitor?The selection of portable monitor is obviously based on quality, price and services. There are many manufacturers specialized in this subject. MeCan is an option. Various brands have different targets. You are expected to hunt on the internet by typing in the item and the service anticipated, e.g."portable monitor OEM", so as to locate the top brands. When you get the manufacturer, you may check the origin of raw materials, the technologies adopted and the services provided. ——————— 4. What exhibitions do portable monitor manufacturers attend?Trade shows producers attend are usually targeted in the business and individuals involved or interested in this business. MeCan generally would execute products and promote testing at commerce displays and exhibitions to acquire businesses or standard view about our offering. Exhibiting in a trade show can be a fantastic way to market.
Deep Learning for Improved Breast Cancer Monitoring Using a Portable Ultrasound Scanner
Deep Learning for Improved Breast Cancer Monitoring Using a Portable Ultrasound Scanner
About 1 in 8 U.S. women will develop invasive breast cancer during their lifetimes. Its the second leading cause of cancer death for women in the U.S. Ultrasound imaging is a noninvasive medical imaging technique used for breast cancer screening. At Insight, I worked on a consulting project with a local start-up company that developed an automated, portable, and wearable ultrasound imaging platform allowing users to perform self-monitoring of breast health. The goal of the project is to automatically detect malignant lesions in ultrasound images. In this blog, Ill describe how this project uses segmentation to detect lesions in an image, and classification to detect whether those lesions are benign or malignant. Data pre-processingFor the segmentation task, radiologists drew contours around the lesions in 240 pairs of raw images. The regions within the contour were highlighted in the mask images (see image above). For the classification task, I had roughly balanced data for benign and malignant cases. Before using the raw data for the training, they were pre-processed as shown in the following figure.The raw images were filtered to suppress noise, then the contrast in the images was enhanced. Since the dataset was small, augmentation was performed on the images, such as horizontally flipping the image, rotation and other deformations. Finally, the images were cropped to the same size.SegmentationSince a few patients had multiple images in the dataset, the data were separated, by patient, into three parts: training (80%), validation (10%), and testing (10%).The model was a modified U-Net and trained on GPU hosted by Amazon Web Services (AWS) EC2 instances. The loss function used is illustrated in the figure below, with A representing the ground truth (manually labeled mask) and B representing the model generated mask. If the mask from the model is completely off, then the intersection/union ratio is 0. If they are completely overlapping, then the ratio is 1. The resulting image (below) shows the ground truth in blue, with the red contour generated by the model. The box plot below shows a summary of the testing results. The average intersection/union ratio is 0. 74, which means that, on average, theres 74% overlap between the target mask and our prediction output. The baseline ratio is 0.06, and is calculated by assuming the model is just randomly guessing. In this case, the intersection is half the size of the average mask size, and the union is half the size of the whole image. ClassificationWhen a radiologist reads an ultrasound image, he first identifies the lesion region in the image, then looks at both the lesion region and surrounding regions for different types of features in order to make a decision. Here, we built a model to mimic this process.The architecture of the model for classification is shown in the following figure. Since there was no mask available in the provided classification dataset, the masks were generated using the model in the previous segmentation step. Raw images were passed through convolutional layers so the model could learn a set of filters to extract features. The mask provided the region of interest and, after, it passed through convolutional layers with the same number of filters as the image branch. For each filter in the image branch, there was a corresponding filter in the mask branch. Some filters in the image branch were important in extracting features from the lesion region, while others were important in extracting features from the background region of the image. The filters in the mask branch can be trained to weight these two regions differently for features learned in the image branch. These two pieces of information were combined and fed into convolutional layers, then fully connected layers, to generate results.In order to see what the model learned after training, we fed it with a fixed mask and a blank image with white noise, as shown in the following figure. To generate an image that represented the malignant features learned, we let the model modify the blank image so that the probability of malignancy is maximized (to almost 1). The following figure shows two examples of the generated images from the model. From these images, we can observe a few things:Both the lesion region and surrounding region contribute to the classification.The lesion regions are more important, since most of the features are concentrated inside these regions. We can see that the model looked for different features for these two regions. This shows that the model indeed learned where and what to look for in the images.The results of the testing dataset are shown in the following confusion matrix. The model is optimized for recall in order to reduce the false negative. The testing accuracy of the model is 0.79 and the recall is 0.85 on test data.DiscussionIn this project, I used deep learning techniques to automatically detect lesion regions and classify the lesion, which can have both cost and time-saving benefits. Currently, patients must make appointments with ultrasound technicians and physicians, which is both time-consuming and costly. In order to reduce cost, combining the device and the mode can reduce the manual process involved. An estimation of the reduction in the manual process can provide a quantified deliverable for this project.The equation used to estimate the manual process is shown in the following:The positive class rate is the prevalence of breast cancer, which is roughly 12%. The recall can be set to 1 by adjusting the threshold of the model. Since the class rate in the training is different than the prevalence rate, the precision from the model is adjusted. The estimated percentage is about 26%, which means that combining the device and model can reduce 74% of the cost for users. ConclusionsThe deep learning model developed in this project can automatically detect lesions in the ultrasound images. By combining the model with the portable scanner, it can produce repeatable images and allow users to monitor their health changes over time, based on their own baseline, for the right diagnosis at the right time.Qin Miao was an Insight Health Data Science Fellow in 2019. He holds a PhD in bioengineering. At Insight, he built deep learning models that can automatically detect lesions in ultrasound images.Are you interested in working on high-impact projects and transitioning to a career in data? Sign up to learn more about the Insight Fellows programs and start your application today
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