matlab-deep-learning-v2

name

matlab-deep-learning-v2

description

MATLAB Deep Learning Toolbox (R2025b). Functions - trainnet, trainingOptions, unet, unet3d, deeplabv3plus, semanticseg, yolov4ObjectDetector, fasterRCNNObjectDetector, maskrcnn, resnet50, efficientnetb0, imagePretrainedNetwork, dlarray, dlfeval, dlgradient, adamupdate, dlnetwork, imageDatastore, augmentedImageDatastore, minibatchqueue. Tasks - train a deep learning model, classify medical images, build a CNN classifier, segment tumors or organs, detect objects or nodules, fine-tune a pretrained network, transfer learning, create a U-Net, train with custom loss, augment training data, deploy model to ONNX, run training on GPU, build a 3D volumetric network, handle class imbalance. Domains - MRI, CT, X-ray, histopathology, dermatology, retinal imaging, cell detection, lesion segmentation, nodule detection, industrial inspection, autonomous systems, satellite imagery, general computer vision, defect detection, quality control imaging.

MATLAB Deep Learning Toolbox (v2.0 -- R2025b)

Expert skill for deep learning in MATLAB, focused on medical image analysis workflows. All examples use the modern R2025b API (trainnet, unet, unet3d, deeplabv3plus).

Cross-Toolbox Note: For image preprocessing (filtering, morphology), use Image Processing Toolbox functions (imgaussfilt, imbinarize, strel). For medical I/O (DICOM, NIfTI), use Medical Imaging Toolbox functions (dicomread, niftiread). For wavelet features, use Wavelet Toolbox functions (wavedec2, wdenoise2).

When to Use This Skill

• Building CNN classifiers for medical diagnosis (X-ray, dermatology, histopathology)
• Semantic segmentation (U-Net, DeepLabv3+ for organ/lesion segmentation)
• Object detection (YOLO, Faster R-CNN for nodule/cell detection)
• Transfer learning with pretrained networks (ResNet, VGG, EfficientNet)
• Custom training loops with dlarray and automatic differentiation
• 3D volumetric deep learning for CT/MRI
• GPU-accelerated training and multi-GPU workflows
• Model deployment (ONNX export, GPU Coder)

Read Before Coding

Task	Knowledge Card	Template Script
Classify images / transfer learning	cards/classification-segmentation.md	scripts/template_transfer_learning_classification.m
Semantic segmentation (U-Net)	cards/classification-segmentation.md	scripts/template_unet_segmentation.m
DeepLabv3+ segmentation	cards/classification-segmentation.md	scripts/template_deeplabv3plus_segmentation.m
3D volumetric segmentation	cards/medical-imaging.md (CRITICAL)	scripts/template_3d_volumetric_segmentation.m
Object detection	cards/detection-instance-seg.md	scripts/template_object_detection_yolov4.m
Instance segmentation	cards/detection-instance-seg.md	scripts/template_maskrcnn_instance_seg.m
Custom training loop	cards/custom-training.md	scripts/template_custom_training_loop.m
Data augmentation	cards/data-pipeline.md	scripts/template_data_augmentation_pipeline.m
Class imbalance	cards/classification-segmentation.md	scripts/template_class_imbalance_handling.m
Custom layers / attention	cards/network-architecture.md	--
GPU / parallel	cards/gpu-parallel.md	--
Deploy / export	cards/deployment.md	scripts/template_model_export_onnx.m
Medical DL workflows	cards/medical-imaging.md (CRITICAL)	scripts/template_3d_volumetric_segmentation.m

---

Critical Rules

Rule 1: Data Type and Normalization

% WRONG: Using uint8 directly -- poor convergence
% CORRECT: Normalize to [0,1] or [-1,1]
images = im2single(uint8Images);  % Now [0,1]
% For pretrained networks expecting ImageNet normalization:
meanRGB = [0.485, 0.456, 0.406];
stdRGB = [0.229, 0.224, 0.225];
images = (im2single(uint8Images) - reshape(meanRGB,1,1,3)) ./ reshape(stdRGB,1,1,3);

Rule 2: Match Input Sizes

inputSize = net.Layers(1).InputSize;  % e.g., [224 224 3]
augDs = augmentedImageDatastore(inputSize(1:2), imds);

Rule 3: dlarray Format Strings

% Format: S=Spatial, C=Channel, B=Batch, T=Time
x = dlarray(data, 'SSCB');    % H*W*C*B (standard image batch)
x = dlarray(data, 'SSSCB');   % H*W*D*C*B (3D volume batch)
x = dlarray(data, 'CBT');     % C*B*T (sequence)
% Common error: wrong format causes dimension mismatch

Rule 4: Gradient Computation

% Gradients only flow through dlarray operations
% WRONG: mask = Y > 0.5;       % Thresholding breaks gradient flow
% CORRECT: mask = sigmoid((Y - 0.5) * 10);  % Differentiable approximation

Rule 5: GPU Memory Management

gpu = gpuDevice;
fprintf('Available: %.2f GB\n', gpu.AvailableMemory/1e9);
% Reduce batch size if OOM; clear GPU memory between experiments
reset(gpuDevice);

---

3D Volumetric Networks (CRITICAL Knowledge)

The unet3d function builds a 3D U-Net and returns a dlnetwork. This is CRITICAL for CT/MRI volumetric segmentation.

% 3D U-Net for volumetric segmentation
imageSize = [128 128 64 1];  % H*W*D*C
numClasses = 3;

% Modern API (R2024b+) -- returns dlnetwork directly
net = unet3d(imageSize, numClasses, ...
    EncoderDepth=3, ...             % Shallower for GPU memory
    NumFirstEncoderFilters=32);     % Fewer filters for 3D

% CRITICAL: Default NumFirstEncoderFilters is 64 (not 32!)
% For 3D, reduce to 32 to fit in GPU memory

% Train with modern API
opts = trainingOptions('adam', ...
    'MaxEpochs', 100, ...
    'MiniBatchSize', 2, ...         % Small batch for 3D
    'InitialLearnRate', 1e-4, ...
    'ExecutionEnvironment', 'gpu');
net = trainnet(ds, net, "crossentropy", opts);

Patch-Based 3D Training (Large Volumes)

% Volumes too large for GPU -> extract patches
patchSize = [64 64 64];
stride = [32 32 32];  % 50% overlap

% Use randomPatchExtractionDatastore for efficient training
patchDs = randomPatchExtractionDatastore(imds, pxds, patchSize, ...
    'PatchesPerImage', 16);

2.5D Approach (Multi-Slice)

% Use adjacent slices as channels (compromise between 2D and 3D)
imageSize = [256 256 5];  % 5 adjacent slices as channels
net = unet(imageSize, 2, EncoderDepth=4);

---

Quick Patterns

Transfer Learning (Classification)

net = resnet50;
lgraph = layerGraph(net);

% Replace final layers (modern API -- no classificationLayer)
numClasses = 4;
lgraph = removeLayers(lgraph, {'fc1000', 'fc1000_softmax', 'ClassificationLayer_fc1000'});
newLayers = [
    fullyConnectedLayer(numClasses, 'Name', 'fc_new')
    softmaxLayer('Name', 'softmax_new')];
lgraph = addLayers(lgraph, newLayers);
lgraph = connectLayers(lgraph, 'avg_pool', 'fc_new');

opts = trainingOptions('adam', ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 32, ...
    'InitialLearnRate', 1e-4, ...
    'ValidationData', valDs, ...
    'Plots', 'training-progress');
net = trainnet(trainDs, dlnetwork(lgraph), "crossentropy", opts);

U-Net Segmentation

imageSize = [256 256 1];
numClasses = 2;  % Background + Lesion

% Modern API -- returns dlnetwork directly
% IMPORTANT: Default NumFirstEncoderFilters = 64 (not 32)
net = unet(imageSize, numClasses, EncoderDepth=4);

% Prepare data
classNames = ["Background", "Lesion"];
pixelLabelIDs = [0, 1];
pxds = pixelLabelDatastore('masks/', classNames, pixelLabelIDs);
ds = combine(imageDatastore('images/'), pxds);

% Train
opts = trainingOptions('adam', ...
    'MaxEpochs', 50, ...
    'MiniBatchSize', 8, ...
    'InitialLearnRate', 1e-3);
net = trainnet(ds, net, "crossentropy", opts);

% Inference
mask = semanticseg(testImage, net);

Custom Training Loop

net = dlnetwork(lgraph);
numEpochs = 50;
learnRate = 1e-3;
[avgGrad, avgSqGrad] = deal([]);
iteration = 0;

for epoch = 1:numEpochs
    shuffle(mbq);
    while hasdata(mbq)
        iteration = iteration + 1;
        [X, Y] = next(mbq);
        [loss, gradients, state] = dlfeval(@modelLoss, net, X, Y);
        net.State = state;
        [net, avgGrad, avgSqGrad] = adamupdate(net, gradients, ...
            avgGrad, avgSqGrad, iteration, learnRate);
    end
end

function [loss, gradients, state] = modelLoss(net, X, Y)
    [Ypred, state] = forward(net, X);
    loss = crossentropy(Ypred, Y);
    gradients = dlgradient(loss, net.Learnables);
end

---

Transform Selection

Task?
+-- Image Classification
|   +-- Binary/Multiclass -> trainnet + softmax + "crossentropy"
|   +-- Multi-label -> custom loop + sigmoid + binary crossentropy
+-- Segmentation
|   +-- Semantic (pixel labels) -> unet / deeplabv3plus + semanticseg
|   +-- Instance (object masks) -> Mask R-CNN (maskrcnn)
|   +-- 3D volumetric -> unet3d
+-- Object Detection
|   +-- Real-time -> YOLO v4 (yolov4ObjectDetector)
|   +-- High accuracy -> Faster R-CNN (fasterRCNNObjectDetector)
|   +-- Small objects -> RetinaNet
+-- Custom Architecture
|   +-- dlnetwork + custom training loop + dlfeval/dlgradient
+-- Generative
    +-- GAN / VAE with custom training loop

Function Quick Reference

Core Training

Function	Purpose
trainnet	Train dlnetwork with loss function string
trainingOptions	Configure optimizer, scheduler, GPU
trainingProgressMonitor	Custom progress display

Segmentation Networks

Function	Purpose	Notes
unet	Create 2D U-Net (dlnetwork)	Default NumFirstEncoderFilters = 64
unet3d	Create 3D U-Net (dlnetwork)	Use NumFirstEncoderFilters=32 for GPU memory
deeplabv3plus	Create DeepLabv3+ (dlnetwork)	Backbone: 'resnet50'
semanticseg	Pixel classification inference	Works on images or datastores

Detection

Function	Purpose
yolov4ObjectDetector	YOLO v4 detector
fasterRCNNObjectDetector	Faster R-CNN
maskrcnn	Mask R-CNN (instance segmentation)
detect / segmentObjects	Run detection / instance seg

Template Scripts

Complete, ready-to-adapt template scripts in scripts/:

Script	Description
template_transfer_learning_classification.m	Fine-tune ResNet-50 or EfficientNet-b0 for medical image classification
template_unet_segmentation.m	2D U-Net for organ/tumor segmentation from CT/MRI slices
template_deeplabv3plus_segmentation.m	DeepLabv3+ with ResNet-50 backbone for histopathology segmentation
template_3d_volumetric_segmentation.m	3D U-Net for volumetric CT/MRI segmentation with patch-based training
template_object_detection_yolov4.m	YOLOv4 for nodule/lesion detection with bounding boxes
template_maskrcnn_instance_seg.m	Mask R-CNN for cell/nuclei instance segmentation
template_custom_training_loop.m	Manual training loop with Dice + CE loss, gradient clipping, early stopping
template_data_augmentation_pipeline.m	Medical augmentation for 2D and 3D data
template_class_imbalance_handling.m	Weighted CE, oversampling, and focal loss for imbalanced datasets
template_model_export_onnx.m	Export dlnetwork to ONNX for cross-platform deployment

Knowledge Cards Summary

Card	Priority	Focus
medical-imaging.md	CRITICAL	3D volumetric networks, DICOM pipelines, modality preprocessing
classification-segmentation.md	Reference	Transfer learning, U-Net config, loss functions, evaluation
detection-instance-seg.md	Reference	YOLO, Faster R-CNN, Mask R-CNN, anchor boxes
custom-training.md	Reference	Advanced patterns: LR scheduling, gradient accumulation, multi-loss
data-pipeline.md	Reference	Custom datastores, medical preprocessing, advanced augmentation
network-architecture.md	Reference	Custom layers, attention, SE blocks
gpu-parallel.md	Reference	GPU memory management, multi-GPU training
deployment.md	Reference	ONNX export, GPU Coder, quantization

See knowledge/INDEX.md for full navigation.

Cross-Toolbox Integration

For preprocessing -> Image Processing Toolbox (imgaussfilt, adapthisteq, imbinarize)

img = adapthisteq(img);         % CLAHE
img = imgaussfilt(img, 1.5);    % Denoise
img = im2single(img);           % Normalize

For wavelet features -> Wavelet Toolbox (wavedec2, wdenoise2)

[C, S] = wavedec2(img, 3, 'db4');
features = cat(3, appcoef2(C,S,'db4'), ...
    detcoef2('h',C,S,1), detcoef2('v',C,S,1), detcoef2('d',C,S,1));

For medical I/O -> Medical Imaging Toolbox (medicalVolume, dicomread, niftiread)

V = medicalVolume('brain.nii');
for k = 1:V.NumTransverseSlices
    slice = extractSlice(V, k, 'transverse');
    prediction = semanticseg(slice, net);
end

---

Verified against MATLAB R2025b