Multi-Instance Learning by Utilizing Structural Relationship among Instances
Multi-Instance Learning(MIL) aims to learn the mapping between a bag of instances and the bag-level label. Therefore, the relationships among instances are very important for learning the mapping. In this paper, we propose an MIL algorithm based on a graph built by structural relationship among instances within a bag. Then, Graph Convolutional Network(GCN) and the graph-attention mechanism are used to learn bag-embedding. In the task of medical image classification, our GCN-based MIL algorithm makes full use of the structural relationships among patches(instances) in an original image space domain, and experimental results verify that our method is more suitable for handling medical high-resolution images. We also verify experimentally that the proposed method achieves better results than previous methods on five bechmark MIL datasets and four medical image datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph Attentionimage-classificationImage ClassificationMedical Image ClassificationSimilar Papers 제목 키워드 기반
ExDBN: Exact learning of Dynamic Bayesian Networks
Causal learning from data has received much attention in recent years. One way of capturing causal relationships is by utilizing Bayesian networks. There, one recovers a weighted directed acyclic graph, in which random v…
Utilizing Language Relatedness to improve Machine Translation: A Case Study on Languages of the Indian Subcontinent
In this work, we present an extensive study of statistical machine translation involving languages of the Indian subcontinent. These languages are related by genetic and contact relationships. We describe the similaritie…
Machine TranslationTranslationRaDL: Relation-aware Disentangled Learning for Multi-Instance Text-to-Image Generation
With recent advancements in text-to-image (T2I) models, effectively generating multiple instances within a single image prompt has become a crucial challenge. Existing methods, while successful in generating positions of…
Text-to-Image GenerationContrastive Learning Subspace for Text Clustering
Contrastive learning has been frequently investigated to learn effective representations for text clustering tasks. While existing contrastive learning-based text clustering methods only focus on modeling instance-wise s…
ClusteringContrastive LearningSemantic SimilaritySemantic Textual Similarity+1Structural Balance of Complex Weighted Graphs and Multi-partite Consensus
The structural balance of a signed graph is known to be necessary and sufficient to obtain a bipartite consensus among agents with friend-foe relationships. In the real world, relationships are multifarious, and the coex…