I am using COBRE brain MRI dataset containing Nifti files. I can visualize them but could not understand how to use them in deep learning with the correct format. I read Nilearn documentation but they have used only one example of .nii file for 1 subject. The question is how to give 100 .nii files to a CNN?
The second thing is how to determine which slice of the file should be used? Should it be the middle of them? Nifti file consists of 150 slices for each subject's brain.
The third thing is how to provide the model with labels? The dataset doesn't contain any mask. How to give the model specific label for a specific file? Should I create a csv file with path of .nii files and their associated label?
Please explain me or suggest me some resources for the same.
hi i recently got into processing of nii files for one of my projects. i could get a break though to some level (preprocessing) not yet to model level.
for your second question, usually an expert visualise the niis and provide the location(s) of the roi(region of interest)
I am currently in the process of parsing the nii into csv format with labels. so the answer to your third question is , we lable the coordinates (x,y,z,c,t) as per the roi locations . (i may need to correct this understanding as i advance on need basis but for now this is the approach to feed the dataset to model i am goin to follow)
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I was working on a face recognition code. I wanted to find the performance metrics (accuracy, precision, etc.) of my algorithm. I've used HaarCascade Algorithm and LBPH FaceRecogniser When I searched for it on the net, I could find only those sources where already existing datasets are taken and the parameters are computed. I want to use the data obtained from training my model (trained from the images folder). A folder named "trainner.yml" is generated automatically after running the file.
is the data from the trainner.yml file my dataset? What is my dataset now and how can I find the confusion matrix
Thanks
I would like to know a couple of things to clear my confusion. I want to work on a medical neuroimage MRI image scans dataset from the ADNI database.
Each Alzheimer's Disease (AD) MRI image scan has multiple slices.
Do I have to separate each image scan slice and label each of them as AD or combine all image scan slices as a one-image scan and label it for classification?
Most of the medical neuroimage DICOM, NfINT, NII, etc., format. Is it mandatory to convert them to png or jpg for the CNN network model or keep it in NfNIT or nii format?
I have read several existing papers on neuroimaging regarding Alzheimer's disease but did not find the above question answer. Even I have sent an email to the research paper writer in reply; I got they can not help on this as they are very busy and mention their sincere apology for that.
It will be very helpful if anyone has the answer to clear my confusion and thought.
Thank you.
You can train with NIfTI, using, for example, TorchIO. There's no need to separate each slice, you can use the 3D image as is.
You can find some examples in the documentation.
Disclaimer: I'm the main developer of TorchIO.
I've noticed that for any tutorial or example of a Keras CNN that I've seen, the input images are numbered, e.g.:
dog0001.jpg
dog0002.jpg
dog0003.jpg
...
Is this necessary?
I'm working with an image dataset with fairly random filenames (the classes come from the directory name), e.g.:
picture_A2.jpg
image41110.jpg
cellofinterest9A.jpg
I actually want to keep the filenames because they mean something to me, but do I need to append sequential numbers to my image files?
No they can be of different names, it really depends on how you load your data. In your case, you can use flow_from_directory to generate the training data and indeed the directory will be the associated class, this is part of ImageDataGenerator.
In the article for creating a dataaset for the TF object detection API [link], users are asked to store an object mask as:
a repeated list of single-channel encoded PNG strings, or a single dense 3D binary tensor where masks corresponding to each object are stacked along the first dimension
Since the article strongly suggests using a repeated list of single-channel encoded PNG strings, I would particularly be interested in knowing how to encode this. My annotations are typically from csv files, which I have no problem in generating the TFRecords file. Are there any instructions somewhere on how to make this conversion?
i make it works with pet dataset , on tensorflow you have 2 way with coco dataset tf record and with pet_tfrecord.
the first took JSON file
the second take XML and PNG
there is one application VGG could make annotations in PNG or in JSON, then you use the directory tree needed, i used the pet dataset example. but finally mask is not displayed, even with the example dataset...
Rather than the array of png, I ended up using a dense tensor, where each pixel value represents a class.
Note,, I’ve seen many other people who didn’t use sequential numbers and ended up having problems later. The idea makes sense, if I have 2 classes, label 1 as 0 and the other as 255. The rationale is that when you see this in grayscale it is obvious what gets labeled 1 or 255. This is impossible when you use 0,1,2,... However, this violates a lot of assumptions in downstream code (e.g. deeplab)
I am trying to train a binary classifier using libsvm.
My data quantity is very large and I need to know of any way I can divide the input data into different files and input to the train function.
So basically I know this :
svm-train train file
I wonder if there's a way to do:
svm-train train_file1 train_file2 train_file3.....
Does anyone know any way to do this??
From the FAQ's of libsvm
For large problems, please specify enough cache size (i.e., -m). You may train only a subset of the data. You can use the program subset.py in the directory "tools" to obtain a random subset.