how to implement 4d resnet in matlab

i am having 4 set of 3d images for 2 classes. i need to implement 4d resnet transfer learning for classification by stacking 4 set of 3d image to a 4d image. My image format is .nii.

Answer 1

[Hari]

Hi Abinayasundari,

I understand you have four sets of 3D images for two classes, and you want to implement a 4D “ResNet” for transfer learning in MATLAB. You want to stack the four sets of 3D images into a 4D image and utilize it for classification.

I assume that you already trained your “ResNet” model. To implement a 4D “ResNet” for transfer learning in MATLAB, you can follow these steps:

1. Load and preprocess the 3D images from each set. Use the `niftiread` function from the Image Processing Toolbox to read the .nii files.

% Specify the path to the .nii file
filePath = 'path_to_your_nii_file.nii';
% Read the .nii file using niftiread
niiData = niftiread(filePath);

2. Stack the pre-processed 3D images from each set into a 4D array. Utilize the `cat` function in MATLAB to concatenate the image sets along the fourth dimension.

% Stack the sets into a 4D array
stackedImages = cat(4, set1, set2, set3, set4);

3. Load a pre-trained “ResNet” model in MATLAB. Modify the last layers of the pre-trained “ResNet” model to suit your classification task. You can use the `replaceLayer` function to modify the model.

net = resnet50; % Load pre-trained ResNet-50 model
numClasses = 2; % Number of classes in your problem
% Modify the last layers of the model
net = replaceLayer(net, 'fc1000', newLayers);

4. Train and fine-tune the modified “ResNet” model using the stacked 4D images. Utilize the `trainNetwork` function to train the model and evaluate the trained model on a separate test set to assess its performance. Use the `classify` function to obtain predictions for new data.

% Train the network
net = trainNetwork(stackedImages, categorical(labels), layers, options);
% Classify the test images
predictedLabels = classify(net, stackedTestImages);

By following the above steps, you will be able to stack 3D images into a 4D image and utilize it for classification.

Refer to the documentation of “niftiread” for more information on reading .nii files in MATLAB: niftiread - MATLAB

For insights into loading a pre-trained “ResNet-50a” model, explore the documentation for resnet50 in MATLAB: resnet50 - MATLAB

For a detailed understanding of specifying training options for deep learning, and how to change the default parameters, refer the MATLAB documentation for “trainingOptions”: trainingOptions - MATLAB.

Hope this helps!