MIT is one of the world’s premier research universities, responsible for all manner of innovative and exciting developments since their founding in 1861. Now MIT researchers have developed a machine-learning algorithm that registers 3D images (like brain scans) 1000-plus times more quickly than traditional methods.
Traditionally, 3D images are created via a technique called medical image registration. This process overlays two images, like MRIs, to compare and contrast anatomical information in great detail – an especially useful tool for doctors to gauge progress with a patient or treatment.
MRI (magnetic resonance imaging) scans consist of hundreds of 2D images, all stacked on top of each other to form 3D images. These images, called “volumes”, contain millions of pixels, or “voxels”. Aligning voxels from multiple volumes is a complex process, made more so by variables like spatial orientations and machine types. Adrian Dalca, a postdoc at Massachusetts General Hospital and CSAIL and co-author of the paper, describes it as “wiggling” the images until the images fit each other. “Mathematically, this optimization procedure takes a long time,” said Dalca – potentially hundreds of hours, if analyzing scans from large populations of data.
This delay is because the algorithms involved never learn from the information they analyze, instead of dismissing all data regarding voxel location after each pair of images. The new algorithm, VoxelMorph, corrects this flaw, registering information from thousands of pairs of images – “Information you should be able to carry over,” explained Guha Balakrishnan, an MIT grad student, and paper co-author – to learn how to align images and estimate optimal alignment parameters. Once the algorithm “learns”, it maps all pixels from one image to another at once, vastly reducing registration times to a couple of minutes via a standard computer.
VoxelMorph uses a common machine-learning approach called a CNN or convolutional neural network. The CNN network is augmented by a spatial transformer, which captures similarities in voxels between MRI scans. It learns from groups of voxels, which it then uses to develop optimized parameters that can be used on any scan pair. All information is gathered in the training phase, with future registrations executed using a single, easily-computed function evaluation.
Another benefit to VoxelMorph is the data is “unsupervised” – it does not require additional information beyond image data to make an accurate reading. Each registration is “smooth”, or without any image distortion or holes, and can be calculated within roughly two minutes via a traditional CPU, or under a second with a graphics processing unit.
Enhanced speed opens a variety of potential application – scanning other parts of the body, for example, or using image registration in close to real-time. The result is a better experience for patients and a powerful tool in doctors’ pockets, all thanks to machine learning.
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The price of this ticker is presumed to bounce back soon, since the longer the ticker stays in the oversold zone, the more promptly an uptrend is expected.
The Stochastic Oscillator demonstrated that the ticker has stayed in the oversold zone for 1 day, which means it's wise to expect a price bounce in the near future.
Following a 3-day Advance, the price is estimated to grow further. Considering data from situations where ELV advanced for three days, in of 322 cases, the price rose further within the following month. The odds of a continued upward trend are .
ELV may jump back above the lower band and head toward the middle band. Traders may consider buying the stock or exploring call options.
ELV moved below its 50-day moving average on September 23, 2024 date and that indicates a change from an upward trend to a downward trend.
The 10-day moving average for ELV crossed bearishly below the 50-day moving average on September 27, 2024. This indicates that the trend has shifted lower and could be considered a sell signal. In of 18 past instances when the 10-day crossed below the 50-day, the stock continued to move higher over the following month. The odds of a continued downward trend are .
Following a 3-day decline, the stock is projected to fall further. Considering past instances where ELV declined for three days, the price rose further in of 62 cases within the following month. The odds of a continued downward trend are .
The Aroon Indicator for ELV entered a downward trend on October 23, 2024. This could indicate a strong downward move is ahead for the stock. Traders may want to consider selling the stock or buying put options.
The Tickeron Profit vs. Risk Rating rating for this company is (best 1 - 100 worst), indicating low risk on high returns. The average Profit vs. Risk Rating rating for the industry is 72, placing this stock better than average.
The Tickeron Valuation Rating of (best 1 - 100 worst) indicates that the company is slightly undervalued in the industry. This rating compares market capitalization estimated by our proprietary formula with the current market capitalization. This rating is based on the following metrics, as compared to industry averages: P/B Ratio (3.054) is normal, around the industry mean (3.063). P/E Ratio (20.460) is within average values for comparable stocks, (17.509). Projected Growth (PEG Ratio) (0.863) is also within normal values, averaging (1.089). Dividend Yield (0.012) settles around the average of (0.020) among similar stocks. P/S Ratio (0.715) is also within normal values, averaging (0.684).
The Tickeron PE Growth Rating for this company is (best 1 - 100 worst), pointing to consistent earnings growth. The PE Growth rating is based on a comparative analysis of stock PE ratio increase over the last 12 months compared against S&P 500 index constituents.
The Tickeron Price Growth Rating for this company is (best 1 - 100 worst), indicating fairly steady price growth. ELV’s price grows at a lower rate over the last 12 months as compared to S&P 500 index constituents.
The Tickeron Seasonality Score of (best 1 - 100 worst) indicates that the company is slightly overvalued in the industry. The Tickeron Seasonality score describes the variance of predictable price changes around the same period every calendar year. These changes can be tied to a specific month, quarter, holiday or vacation period, as well as a meteorological or growing season.
The Tickeron SMR rating for this company is (best 1 - 100 worst), indicating weak sales and an unprofitable business model. SMR (Sales, Margin, Return on Equity) rating is based on comparative analysis of weighted Sales, Income Margin and Return on Equity values compared against S&P 500 index constituents. The weighted SMR value is a proprietary formula developed by Tickeron and represents an overall profitability measure for a stock.
The average fundamental analysis ratings, where 1 is best and 100 is worst, are as follows
a provider of life, hospital and medical insurance plans
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