@Aliktk (Ali Nawaz) posted a new topic, subject "Can I load whole 3D MRI image into my memory without performing patching or subsampling?". It read:

   I have 17 GB of data collected from ADNI where I am going to classify them without any preprocessing and augmentation steps. I load a single image using `nibable` library. Now I am trying to load all the `.nii` format files from my directory.  
    For this purpose I already studied the basic dataloading method shown in WRITING CUSTOM DATASETS and also studied custom dataloader for 3D files .  
    Please help me someone with some sort of code which help me to understand that I am on right path or I have to change the direction of implementing it?

After which, @Balamurali_M (balamurali) added

    Are you planning to train the model with 2d or 3d ?

To which @Aliktk (Ali Nawaz) replied

    I will train my CNN modal using 3D data

After which, @Aliktk (Ali Nawaz) added

    **Please Help!!!**  
    I am stuck here to load the data from the directory for that please specify some direction.

After which, @Balamurali_M (balamurali) added

    What is the best way to load niftii images (.nii) using pytorch dataloader?
    
    > Save the nib file to h5 file. Create a list with [(h5_file_name,slice_no),(h5_file_name,slice_no)]. Iterate this using dataloader, during runtime read the h5 file again and take out the slice. Let me know if you understand this, else i will share the pseudocode. 
    
    Will this help ?

@Aliktk (Ali Nawaz) replied

    I can't get this I am not too much expert in pseudocode  
    I can load a single image but I can save the file in h5 file using **HDF5** software which convert and save into h5 file.? Does It will be ok ?  
    If can share the pseudocode than It will help more to understand your point.  
    Thanks for fast reply


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