Deep learning (DL) requires large amounts of labeled data, which is extremely time-consuming and labor-intensive to obtain for medical image segmentation tasks. Meta-learning focuses on developing learning strategies that enable quick adaptation to new tasks with limited labeled data. However, rich-class medical image segmentation datasets for constructing meta-learning multi-tasks are currently unavailable. In addition, data collected from various healthcare sites and devices may present significant distribution differences, potentially degrading model's performance. In this paper, we propose a task augmentation-based meta-learning method for retinal image segmentation (TAMS) to meet labor-intensive annotation demand. A retinal Lesion Simulation Algorithm (LSA) is proposed to automatically generate multi-class retinal disease datasets with pixel-level segmentation labels, such that meta-learning tasks can be augmented without collecting data from various sources. In addition, a novel simulation function library is designed to control generation process and ensure interpretability. Moreover, a generative simulation network (GSNet) with an improved adversarial training strategy is introduced to maintain high-quality representations of complex retinal diseases. TAMS is evaluated on three different OCT and CFP image datasets, and comprehensive experiments have demonstrated that TAMS achieves superior segmentation performance than state-of-the-art models.

Semidirected networks have received interest in evolutionary biology as the appropriate generalization of unrooted trees to networks, in which some but not all edges are directed. Yet these networks lack proper theoretical study. We define here a general class of semidirected phylogenetic networks, with a stable set of leaves, tree nodes and hybrid nodes. We prove that for these networks, if we locally choose the direction of one edge, then globally the set of directed paths starting at this edge is stable across all choices to root the network. We define an edge-based representation of semidirected phylogenetic networks and use it to define a dissimilarity between networks, which can be efficiently computed in near-quadratic time. Our dissimilarity extends the widely-used Robinson-Foulds distance on both rooted trees and unrooted trees. After generalizing the notion of tree-child networks to semidirected networks, we prove that our edge-based dissimilarity is in fact a distance on the space of tree-child semidirected phylogenetic networks.

In the era of big data with exponential growth in data volume, how to reduce data security issues such as data leakage caused by machine learning is a hot area of recent research. The existing privacy budget allocation strategies are usually only suitable for data applications in specific spaces and cannot meet users' personalized needs for privacy budget allocation. Therefore, a linear privacy budget allocation strategy is proposed. The strategy assigns each layer a linearly increasing privacy budget from the root of the decision tree to the bottom by adjusting the coefficient or constant term. Combining this strategy with the random forest algorithm, a random forest algorithm based on linear privacy budget allocation (DiffPRF_linear) is formed. Experimental results show that the proposed algorithm can realize uniform, arithmetic, and geometric privacy budget allocation policy effects and can also achieve better classification effects than the former, which not only meets the needs of users to protect private data personalized but also maintains high classification accuracy.