For brain tumour segmentation, deep learning models can achieve human expert-level performance given a large amount of data and pixel-level annotations. However, the expensive exercise of obtaining pixel-level annotations for large amounts of data is not always feasible, and performance is often heavily reduced in a low-annotated data regime. To tackle this challenge, we adapt a mixed supervision framework, vMFNet, to learn robust compositional representations using unsupervised learning and weak supervision alongside non-exhaustive pixel-level pathology labels. In particular, we use the BraTS dataset to simulate a collection of 2-point expert pathology annotations indicating the top and bottom slice of the tumour (or tumour sub-regions: peritumoural edema, GD-enhancing tumour, and the necrotic / non-enhancing tumour) in each MRI volume, from which weak image-level labels that indicate the presence or absence of the tumour (or the tumour sub-regions) in the image are constructed. Then, vMFNet models the encoded image features with von-Mises-Fisher (vMF) distributions, via learnable and compositional vMF kernels which capture information about structures in the images. We show that good tumour segmentation performance can be achieved with a large amount of weakly labelled data but only a small amount of fully-annotated data. Interestingly, emergent learning of anatomical structures occurs in the compositional representation even given only supervision relating to pathology (tumour).

翻译：对于脑肿瘤分割，深度学习模型在拥有大量数据和像素级标注的情况下可以达到人类专家级的性能。然而，为大量数据获取像素级标注的昂贵过程并不总是可行的，且在标注数据稀缺的情况下，性能通常大幅下降。为应对这一挑战，我们改进了一种混合监督框架vMFNet，通过无监督学习和弱监督学习，结合非穷尽像素级病理标注，学习鲁棒的组合表示。具体而言，我们利用BraTS数据集模拟一套2点专家病理标注，这些标注指示每个MRI体积中肿瘤（或肿瘤子区域：瘤周水肿、钆增强肿瘤以及坏死/非增强肿瘤）的顶部和底部切片，并据此构建弱图像级标签，表明图像中是否存在肿瘤（或肿瘤子区域）。然后，vMFNet通过可学习的组合vMF核捕捉图像中的结构信息，使用von-Mises-Fisher (vMF)分布对编码后的图像特征进行建模。我们证明，在拥有大量弱标注数据但仅有少量完全标注数据的情况下，可以实现良好的肿瘤分割性能。有趣的是，即使仅依据病理（肿瘤）相关的监督信息，组合表示中也会涌现出解剖结构的学习。