Standard modern machine-learning-based imaging methods have faced challenges
in medical applications due to the high cost of dataset construction and,
thereby, the limited labeled training data available. Additionally, upon
deployment, these methods are usually used to process a large volume of data on
a daily basis, imposing a high maintenance cost on medical facilities. In this
paper, we introduce a new neural network architecture, termed LoGoNet, with a
tailored self-supervised learning (SSL) method to mitigate such challenges.
LoGoNet integrates a novel feature extractor within a U-shaped architecture,
leveraging Large Kernel Attention (LKA) and a dual encoding strategy to capture
both long-range and short-range feature dependencies adeptly. This is in
contrast to existing methods that rely on increasing network capacity to
enhance feature extraction. This combination of novel techniques in our model
is especially beneficial in medical image segmentation, given the difficulty of
learning intricate and often irregular body organ shapes, such as the spleen.
Complementary, we propose a novel SSL method tailored for 3D images to
compensate for the lack of large labeled datasets. The method combines masking
and contrastive learning techniques within a multi-task learning framework and
is compatible with both Vision Transformer (ViT) and CNN-based models. We
demonstrate the efficacy of our methods in numerous tasks across two standard
datasets (i.e., BTCV and MSD). Benchmark comparisons with eight
state-of-the-art models highlight LoGoNet's superior performance in both
inference time and accuracy.

本研究介绍了一种名为 LoGoNet 的新型神经网络架构，通过自监督学习方法解决了现有机器学习图像处理方法在医学应用中的挑战，提高了医学影像分割的性能。