Radiology Machine Learning. Multi-Image Segmentation with TransUNet

In this article, we discussed how to use the TransUNet architecture for image segmentation in radiology. We started by loading the dataset from Activeloop's Hub using Deep Lake and then proceeded to filter out images containing only humans. After that, we fine-tuned a pre-trained TransUNet model on our filtered dataset. Finally, we visualized the segmentation masks predicted by the trained model. The key takeaways from this article are: 1. Pre-trained TransUNet can be used in radiology machine learning projects. 2. The TransUnet architecture is SOTA regarding image segmentation, as it can model hard-to-find anomalies. 3. Because the network uses both CNN and a self-attention mechanism, it can find local features and preserve them longer, thus enabling it to find features that would typically be out-of-reach. 4. Deep Lake provides an efficient way to load, query, visualize, & stream the data for training and testing purposes. 5. The queried dataset can be saved and later materialized, which enables reproducibility. In conclusion, TransUNet is a powerful architecture that combines CNNs with self-attention mechanisms to achieve state-of-the-art performance in image segmentation tasks. By using pre-trained models and fine-tuning them on domain-specific data, we can leverage the strengths of both architectures to improve the accuracy of our predictions.