Title: Prototype-Based Approach for One-Shot Segmentation of Brain Tumors using Few-Shot Learning Show Chinese title

Title: 基于原型的方法用于脑肿瘤的单次分割的少样本学习

Abstract: The potential for augmenting the segmentation of brain tumors through the use of few-shot learning is vast. Although several deep learning networks (DNNs) demonstrate promising results in terms of segmentation, they require a substantial quantity of training data in order to produce suitable outcomes. Furthermore, a major issue faced by most of these models is their ability to perform well when faced with unseen classes. To address these challenges, we propose a one-shot learning model for segmenting brain tumors in magnetic resonance images (MRI) of the brain, based on a single prototype similarity score. Leveraging the recently developed techniques of few-shot learning, which involve the utilization of support and query sets of images for training and testing purposes, we strive to obtain a definitive tumor region by focusing on slices that contain foreground classes. This approach differs from other recent DNNs that utilize the entire set of images. The training process for this model is carried out iteratively, with each iteration involving the selection of random slices that contain foreground classes from randomly sampled data as the query set, along with a different random slice from the same sample as the support set. In order to distinguish the query images from the class prototypes, we employ a metric learning-based approach that relies on non-parametric thresholds. We employ the multimodal Brain Tumor Image Segmentation (BraTS) 2021 dataset, which comprises 60 training images and 350 testing images. The effectiveness of the model is assessed using the mean dice score and mean Intersection over Union (IoU) score. Show Chinese abstract

Abstract: 通过使用少量学习来增强脑肿瘤分割的潜力巨大。 尽管有几个深度学习网络（DNN）在分割方面表现出有希望的结果，但它们需要大量的训练数据才能产生合适的结果。 此外，这些模型面临的一个主要问题是，在面对未见过的类别时表现不佳。 为了解决这些挑战，我们提出了一种基于单一原型相似度分数的一次性学习模型，用于分割脑部磁共振成像（MRI）中的脑肿瘤。 利用最近开发的少量学习技术，该技术涉及支持集和查询集图像的利用以进行训练和测试，我们努力通过关注包含前景类别的切片来获得明确的肿瘤区域。 这种方法不同于其他最近使用的整个图像集的DNN。 该模型的训练过程是迭代进行的，每次迭代都包括从随机采样的数据中选择包含前景类别的随机切片作为查询集，以及来自同一样本的不同随机切片作为支持集。 为了区分查询图像与类原型，我们采用了一种基于度量学习的方法，该方法依赖于非参数阈值。 我们采用了多模态脑肿瘤图像分割（BraTS）2021数据集，其中包含60个训练图像和350个测试图像。 该模型的有效性通过平均Dice评分和平均交并比（IoU）评分进行评估。