wellallyTech

Posted on Oct 10

Skin Health at Your Fingertips: Fine-Tuning Vision Transformers (ViT) for Real-Time Mobile Screening 🩺💻

#python #machinelearning #visiontransformer #programming

In recent years, Medical Image Analysis has undergone a massive shift. While Convolutional Neural Networks (CNNs) have been the reigning kings of computer vision, the Vision Transformer (ViT) is now challenging the throne. By treating image patches as "tokens" (just like words in a sentence), ViT captures long-range dependencies that traditional kernels often miss. In this tutorial, we will explore a high-performance PyTorch implementation of a skin lesion screening system using Transfer Learning, ultimately deploying it for ultra-fast on-device inference.

Whether you are building a personal health monitor or an enterprise-grade diagnostic tool, understanding how to optimize these heavy transformers for mobile is a game-changer. For a deeper look into production-ready AI patterns and advanced medical imaging architectures, you should definitely bookmark the WellAlly Blog, where we discuss scaling these models for millions of users.

The Architecture: From Pixels to Predictions

Before we dive into the code, let's visualize how the data flows from a raw image of a skin lesion to a high-confidence classification on a mobile device.

graph TD
    A[Input Image: 224x224] --> B[Patch Partition & Linear Projection]
    B --> C[Position Embedding]
    C --> D[Transformer Encoder Blocks x12]
    D --> E[MLP Head / Classification Layer]
    E --> F{Probability Distribution}
    F -->|Fine-Tuning| G[PyTorch Model .pt]
    G --> H[Quantization Tooling]
    H --> I[CoreML / ONNX Export]
    I --> J[On-Device Real-time Inference]

    style A fill:#f9f,stroke:#333,stroke-width:2px
    style J fill:#00ff00,stroke:#333,stroke-width:2px

Prerequisites 🛠️

To follow this advanced guide, you'll need the following stack:

PyTorch: The backbone for our model training.
Vision Transformer (ViT): Specifically vit_b_16 (Base model with 16x16 patches).
CoreML Tools / ONNX: For hardware-accelerated mobile deployment.
Dataset: The HAM10000 dataset (Human Against Machine) is the gold standard for skin lesion tasks.

Step 1: Fine-Tuning the Vision Transformer

Standard ViTs are trained on ImageNet. To make them work for dermatology, we need to replace the classification head and unfreeze the top layers for domain-specific feature extraction.

import torch
import torch.nn as nn
from torchvision import models, transforms

def get_vit_model(num_classes=7):
    # Load pre-trained ViT
    model = models.vit_b_16(weights=models.ViT_B_16_Weights.IMAGENET1K_V1)

    # Adjust the heads (MLP Head)
    # ViT_B_16 uses 768 hidden units
    model.heads = nn.Sequential(
        nn.Linear(768, 512),
        nn.ReLU(),
        nn.Dropout(0.3),
        nn.Linear(512, num_classes)
    )
    return model

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = get_vit_model().to(device)

print(f"🚀 Model initialized with {sum(p.numel() for p in model.parameters())} parameters.")

Step 2: Training Logic (The "Magic" Sauce)

Medical data is often imbalanced. When training, use a WeightedRandomSampler and a lower learning rate for the transformer backbone to prevent "catastrophic forgetting."

# Quick training snippet
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5, weight_decay=0.01)
criterion = nn.CrossEntropyLoss()

# Note: In a real scenario, use a Learning Rate Scheduler (e.g., CosineAnnealing)
# For more production-grade training scripts, check out https://www.wellally.tech/blog

Step 3: Quantization & Mobile Export 📱

A raw ViT-B model is roughly 330MB. That’s too heavy for a mobile app. We need to convert it to CoreML (for iOS) or ONNX (for Android/Web) and apply 8-bit quantization.

Exporting to CoreML (iOS Optimization)

import coremltools as ct

model.eval()
example_input = torch.rand(1, 3, 224, 224).to("cpu")
traced_model = torch.jit.trace(model.to("cpu"), example_input)

# Convert to CoreML
mlmodel = ct.convert(
    traced_model,
    inputs=[ct.ImageType(name="input_1", shape=example_input.shape)],
    classifier_config=ct.ClassifierConfig(["Actinic", "BCC", "BKL", "DF", "MEL", "NV", "VASC"])
)

# Apply quantization to reduce size by 75%
from coremltools.models.neural_network import quantization_utils
quantized_model = quantization_utils.quantize_weights(mlmodel, nbits=8)
quantized_model.save("SkinClassifier.mlmodel")

The "Official" Way: Advanced Patterns 🥑

While the steps above get you a working prototype, deploying medical AI requires rigorous validation, specialized data augmentation (like Albumentations), and handling edge cases in lighting conditions.

If you are looking for:

FP16 vs INT8 performance benchmarks on mobile chips.
Integrating Saliency Maps (Grad-CAM) so doctors can see why the AI made a decision.
Architecting a HIPAA-compliant backend for these models.

You should explore the resources at WellAlly Tech Blog. It's an incredible hub for developers looking to bridge the gap between "it works on my machine" and "it works in the clinic."

Conclusion

Transitioning from CNNs to Vision Transformers for skin lesion screening unlocks a new level of accuracy by leveraging global context. By combining PyTorch's flexibility with CoreML/ONNX's efficiency, we can bring life-saving technology directly to the palm of a user's hand.

What's next?

Try adding Data Augmentation (Rotate, Flip, Color Jitter) to improve robustness.
Experiment with MobileViT for even faster performance.
Drop a comment below if you want a Part 2 on building the Flutter/Swift UI for this!

Happy coding! 🚀🌟

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