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Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation
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Vision transformers are effective deep learning models for vision tasks, including medical image segmentation. However, they lack efficiency and translational invariance, unlike convolutional neural networks (CNNs). To model long-range interactions in 3D brain lesion segmentation, we propose an all-convolutional transformer block variant of the U-Net architecture. We demonstrate that our model provides the greatest compromise in three factors: performance competitive with the state-of-the-art; parameter efficiency of a CNN; and the favourable inductive biases of a transformer. Our public implementation is available at https://github.com/liamchalcroft/MDUNet .
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Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning
Sequence-invariant contrastive learning on simulated MRI contrasts yields a 3D encoder that improves low-data segmentation and denoising over a synthetic-MPRAGE baseline.
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