Hi scientists!
I need to collect a number of PET-MR images from the ADNI website. I need to measure the SUVR values (Standardized Uptake Value) and compare two groups.
My problem is I cannot find the height and weight of the subjects, and I need them to measure the SUVR.
Do you know if ADNI provides these two information for the subjects? and how may I find them?
Thanks in advance,
Taha
Related
I have come across a problem.
I’m not asking for help how to construct what I’m searching for, but only to guide me to what I’m looking for! 😊
The thing I want to create is some sort of ‘Sorting Algorithm/Mechanism’.
Example:
Imagine I have a database with over 1000 pictures of different vehicles.
A person sees a vehicle, he now tries to get as much information and details about that vehicle, such as:
Shape
number of wheels
number and shape of windows
number and shape of light(s)
number and shape of exhaust(s)
Etc…
He then gives me all information about that vehicle he saw. BUT! Without telling me anything about:
Make and model.
…
I will now take that information and tell my database to sort out every vehicle so that it arranges all 1000 vehicle by best match, based by the description it have been given.
But it should NOT exclude any vehicle!
So…
If the person tells me that the vehicle only has 4 wheels, but in reality it has 5 (he might not have seen the fifth wheel) it should just get a bad score in the # of wheels.
But if every other aspect matches that vehicle perfect it will still get a high score.
That way we don’t exclude the vehicle that he has seen, and we still have a change to find the correct vehicle.
The whole aspect of this mechanism is to, as said, sort out the most, so instead of looking through 1000 vehicles we only need to sort through the best matches which is 10 to maybe 50 vehicles out of a 1000 (hopefully).
I tried to describe it the best I could in a language that isn’t ‘my father’s tongue’. So bear with me.
Again, I’m not looking for anybody telling me how to make this algorithm, I’m pretty sure nobody even wants of have the time to do that for me, without getting paid somehow...
But I just need to know where to look regarding learning and understanding how to create this mess of a mechanism.
Kind regards
Gent!
Assuming that all your pictures have been indexed with the relevant fields (number of wheels, window shapes...), and given that they are not too numerous (a thousand is peanuts for a computer), you can proceed as follows:
for every criterion, weight the possible discrepancies (e.g. one wheel too much costs 5, one wheel too few costs 10, bad window shape costs 8...). Make this in a coherent way so that the costs of the criteria are well balanced.
to perform a search, evaluate the total discrepancy cost of every car, and sort the values increasingly. Report the first ten.
Technically, what you are after is called a "nearest neighbor search" in a high dimensional space. This problem has been well studied. There are fast solutions but they are extremely complex, and in your case are absolutely not worth using.
The default way of doing this for example in artificial intelligence is to encode all properties as a vector and applying certain weights to each property. The distance can then be calculated using any metric you like. In your case manhatten-distance should be fine. So in pseudocode:
distance(first_car, second_car):
return abs(first_car.n_wheels - second_car.n_wheels) * wheels_weight+ ... +
abs(first_car.n_windows - second_car.n_windows) * windows_weight
This works fine for simple properties like the number of wheels. For more complex properties like the shape of a window you'll probably need to split it up into multiple attributes depending on your requirements on similarity.
Weights are usually picked in such a way as to normalize all values, if their range is known. Optionally an additional factor can be multiplied to increase the impact of a specific attribute on the overall distance.
I am using the Facenet algorithm for face recognition. I want to create application based on this, but the problem is the Facenet algorithm returns an array of length 128, which is the face embedding per person.
For person identification, I have to find the Euclidian difference between two persons face embedding, then check that if it is greater than a threshold or not. If it is then the persons are same; if it is less then persons are different.
Let's say If I have to find person x in the database of 10k persons. I have to calculate the difference with each and every person's embeddings, which is not efficient.
Is there any way to store this face embedding efficiently and search for the person with better efficiency?
I guess reading this blog will help the others.
It's in detail and also covers most aspects of implementation.
Face recognition on 330 million faces at 400 images per second
Recommend you to store them in redis or cassandra. They will overperform than relational databases.
Those key-value stores can store multidimensional vector as a value.
You can find embedding vectors with deepface. I shared a sample code snippet below.
#!pip install deepface
from deepface import DeepFace
img_list = ["img1.jpg", "img2.jpg", ...]
model = DeepFace.build_model("Facenet")
for img_path in img_list:
img_embedding = DeepFace.represent(img_path, model = model)
#store img_embedding into the redis here
Sounds like you want a nearest neighbour search. You could have a look at the various space partitioning data structures like kd-trees
First make a dictionary with 10000 face encodings as it is shown at Face_recognition sample, then store it as pickle-file. While loaded in memory it will take a sacond to find distance between X face encoding and that 10000 pre-encoded ones. take a look how it works I'm operating with millions of faces in such way.
I have the following table of Parts which are sold for a particular job, which is the Order Number.
I am trying to extract just the Description of the most expensive part so that I can put it onto a single value card.
I have tried for a day mucking around with CALCULATE, MAX, TOP, SELECTEDVALUE, I cant seem to figure it out. I'm sure it is something simple too...
Would appreciate it if somebody can help me retrieve it in a way that I can see what I missed and learn for future.
My page is filtered by DrillThrough on the Order Number which filters the parts list for me.
Essentially, I just want the card to show 'PUMP,DTH,ELE'. My approach was to just select the top 1 row when the parts list is sorted descending by Amount in LC but it so far has not been as simple as that :(
Should it be a calculated column or measure on my Order table which has that string?
You should be able to create a measure that does this and then place that measure on a card.
Most Expensive Part = LOOKUPVALUE(Parts[Description],Parts[Amount],MAX(Parts[Amount]))
The MAX(Parts[Amount] piece gives you the maximal amount. Then you look up the description corresponding to that amount.
I am going to develop an app for Market Basket Analysis (using apriori algorithm) and I found a dataset which has more than 90,000 Transaction records .
the problem is this dataset doesn't have the name of items in it and only contains the barcode of the items .
I just start the project and doing research on apriori algorithm , can anyone help me about this case , how is the best way to implement this algorithm using the following dataset ?
these kind of datasets are consider critical information and chain stores will not give you these information but you can generate some sample dataset yourself using SQL Server .
The algorithm is defined independent of the identifiers used for the object. Also, you didn't post the 'following data set' :P If your problem is that the algorithm expects your items to be numbered 0,1,2,... then just scan your data set and map each individual barcode to a number.
If you're interested, there's been some papers on how to represent frequent item sets very efficiently: http://www.google.de/url?sa=t&source=web&cd=1&ved=0CB8QFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.163.4827%26rep%3Drep1%26type%3Dpdf&ei=QdVuTsn7Cc6WmQWD7sWVCg&usg=AFQjCNGDG8etNN2B4GQ52pSNIfQaTH7ajQ&sig2=7r3buh8AcfJmn2CwjjobAg
The algorithm does not need the name of the items.
I love to work on AI optimization software (Genetic Algorithms, Particle Swarm, Ant Colony, ...). Unfortunately I have run out of interesting problems to solve. What problem would you like to have solved?
This list of NP complete problems should keep you busy for a while...
How about the Hutter Prize?
From the entry on Wikipedia:
The Hutter Prize is a cash prize
funded by Marcus Hutter which rewards
data compression improvements on a
specific 100 MB English text file.
[...]
The goal of the Hutter Prize is to
encourage research in artificial
intelligence (AI). The organizers
believe that text compression and AI
are equivalent problems.
Basically the idea is that in order to make a compressor which is able to compress data most efficiently, the compressor must be, in Marcus Hutter's words, "smarter". For more information on the relation between artificial intelligence and compression, see the Motivation and FAQ sections of the Hutter Prize website.
Does the Netflix Prize count?
I would like my bank balance optimised so that there is as much money as possible left at the end of the month, instead of the other way round.
What about the Go Game ?
Here's an interesting practical problem I came up while tinkering with color quantization and image compression.
The basic idea is that I would like a program to which I give a picture and it reduces the amount of colors is it as much as possible without me noticing it. Since every person has a different sensitivity of the eye (and eyes have different sensitivity of red/green/blue intensities), it should be possible to specify this sensitivity threshold in some way.
In other words, in a truecolor picture, replace every pixel's color with another color so that:
The total count of different colors in a picture would be the smallest possible; and
Every new pixel would have it's color no further from the original color than some user-specified value D.
The D can be defined in different ways, pick your favorite. For example:
Separate red, green and blue components for specifying the maximum possible deviation for each of them (for every pixel you get a rectangular cuboid of valid replacement values);
A real number which would represent the maximum allowable distance in the RGB cube (for every pixel you get a sphere of valid replacement values);
Something inbetween or completely different.
Most efficient solution to a given set of Sudoku puzzles. (excluding brute-force methods)