Detailed information on the dataset is available in the "Data Description" text. Each number is indeed a separate case or patient if you will. Think of the numbers given as the patient ID numbers. So patients numbered 0, 2, 3, 5 etc are in the training dataset, patients numbered 1, 13, 15, 27 etc are in the public test - or validation dataset. Whatever number left is in the private test dataset that is used for grading.
For each patient there are four different types of MRI images for each case, that is what Flair, T1W, T1Gd, T2 are. Each MRI is made of several images called "slices", which are the several images in each of these folders (FLAIR, T1W, T1Gd, T2). What I mean is the collection of images of a FLAIR folder is what makes a FLAIR MRI.
"BraTS21ID" looks like a reference to the study itself that the publication was named after: "The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification". Competition is probably based on this paper.
As per implementation, I am not sure what is allowed or what is required in this particular Kaggle competition, but one simple approach is probably using 3D convolutions. 740
I did not check the details of each folder but usually an MRI consist of several "slices", which at the end of the day are put together (I mean literally placed side by side, like this) to get a single image. You can try to do this for all images, and at the end of the day, place each final image created on top of each other to obtain a 3D tensor.
Let me be a little more specific about the process: My main goal is to obtain a final image of this form. If all folders contain 200 images, it is probably a good idea to lower this number. At the end of the day, we want to put all images side by side to get a single NxM dimensional image, where N,M represent number of small images (slices) in each row, column. So, for example we can get a 5X5 image - an image that represents one of the MRI types - for example FLAIR - that contains 25 small images within. In order to lower the number of images this way, one can simply take the average of each pixel value in every 8 images - So images numbered 1-8 would form a single image, 9-16 another and so on.
We can go ahead and apply the same procedure to all 4 types of MRIs. So, we end up with 4 final images for each MRI type, each of which has 5 rows and 5 columns of smaller images - the slices we took average of. We can now stack those on top of each other to get a 3 Dimensional tensor, which can be used as an input to 3D Convolutions. 740