I’m starting a research project on identifying tumor boundaries in MRI scans. While U-Net (CNN-based) has been the standard, I’m reading a lot about Vision Transformers (ViT) lately. For medical imaging where global context is crucial, is it worth the extra computational cost to switch to a transformer-based architecture like Swin-Unet or TransUNet?
3 answers
In medical imaging, global context is indeed everything. Traditional CNNs have a limited receptive field, meaning they focus on local textures but might miss the broader structural relationship of an organ. TransUNet is a fantastic hybrid because it uses CNN layers to extract high-resolution spatial features while using Transformers to model long-range dependencies. However, be prepared for a much longer training time and the need for a high-end GPU with at least 24GB VRAM. If your dataset is smaller than 1,000 images, stick to a heavily regularized U-Net to avoid overfitting.
Have you considered the latency requirements for your final application? While ViTs offer superior global modeling, their quadratic complexity can make real-time inference a nightmare if you don't have the right hardware stack.
CNNs are still very robust. If you use a ResNet backbone with an attention gate (Attention U-Net), you can get close to Transformer performance with half the training parameters.
I agree with Susan. Sometimes the "hottest" tech isn't the most practical. I've found that data quality and precise labeling often impact medical AI performance more than the specific model architecture.
Robert, for research purposes, latency is usually secondary to dice coefficient scores. For tumor segmentation, even a 1% increase in accuracy is worth the compute. I suggest James looks into the 'SegFormer' architecture as well. It’s more efficient than the original ViT and has shown impressive results on the Multi-Atlas Abdomen Labeling challenge recently.