Multiple instance learning has achieved remarkable results in whole slide image (WSI) classification. However, constrained by patient privacy and the scarcity of cancer data, the insufficient quantity of data poses a challenge in training models with the extensive parameters required for large-pixel WSIs, giving rise to the requirement of few-shot WSI classification. Recently, pre-trained vision-language models (preVLM) have demonstrated great superiority in few-shot WSI classification tasks due to their good transfer-learning and few-shot capabilities. Therefore, we propose an Image and Text Joint Prompts for few-shot whole slide image classification method, ITJP, to construct a few-shot WSI classification model under the multiple instance learning framework. ITJP utilizes the pre-VLM to extract instance features under the unsupervised setting, constructs image prompts to guide the aggregation of instance features into bag features, and subsequently guides the classification of bag features by text prompts. Specifically, we propose an image prototype guided aggregation strategy, where image prototype is obtained by clustering patches extracted from representative WSIs. The image prototype guides the aggregation of instance features and is directly compared with the image patches to achieve more accurate similarity, thereby enhancing the focus on classification-relevant features. Furthermore, low-rank linear transformation is designed to obtain variable image prototype, enhancing adaptability in specific WSI classification task and enabling effective adaptation to the few-shot scenario. We conduct extensive experiments on two WSI datasets to demonstrate the significant performance of the ITJP for few-shot WSI classification.

Few-shot knowledge graph completion (FKGC) addresses the long-tail problem of relations by leveraging a few observed support entity pairs to infer unknown facts for tail-located relations. Learning the relation representation of entity pairs and evaluating the match of query and support entity pairs are the two key steps of FKGC. Existing methods learn the representation of entity pairs by either aggregating neighbors of entities or integrating relation representations in the connected paths from head to tail. However, in few-shot scenarios, the limited number of support entity pairs and insufficient structural information with a single neighborhood topology will lead to matching failure. To this end, we consider the star and ring topological information for a given entity pair: (1) Entity neighborhood, which captures multi-hop neighbors of entities; (2) Relational path, which characterizes compound relation forms. Furthermore, to effectively fuse the two kinds of heterogeneous topological information, we design the multi-aggregator and the fine-grained path correlation matching algorithm to obtain more delicate and balanced matching. Based on the proposed relational path correlation matching module, we propose the relation adaptive network to solve the few-shot temporal knowledge graph completion problem. The experimental results show that our method continuously outperforms the state-of-the-art methods.