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Our research addresses class imbalance issues in heterogeneous graphs using graph neural networks (GNNs). We propose a novel method combining the strengths of Generative Adversarial Networks (GANs) with GNNs, creating synthetic nodes and edges that effectively balance the dataset. This approach directly targets and rectifies imbalances at the data level. The proposed framework resolves issues such as neglecting graph structures during data generation and creating synthetic structures usable with GNN-based classifiers in downstream tasks. It processes node and edge information concurrently, improving edge balance through node augmentation and subgraph sampling. Additionally, our framework integrates a threshold strategy, aiding in determining optimal edge thresholds during training without time-consuming parameter adjustments. Experiments on the Amazon and Yelp Review datasets highlight the effectiveness of the framework we proposed, especially in minority node identification, where it consistently outperforms baseline models across key performance metrics, demonstrating its potential in the field.

Deep learning-based methods are gaining traction in digital pathology, with an increasing number of publications and challenges that aim at easing the work of systematically and exhaustively analyzing tissue slides. These methods often achieve very high accuracies, at the cost of requiring large annotated datasets to train. This requirement is especially difficult to fulfill in the medical field, where expert knowledge is essential. In this paper we focus on nuclei segmentation, which generally requires experienced pathologists to annotate the nuclear areas in gigapixel histological images. We propose an algorithm for instance segmentation that uses pseudo-label segmentations generated automatically from point annotations, as a method to reduce the burden for pathologists. With the generated segmentation masks, the proposed method trains a modified version of HoVer-Net model to achieve instance segmentation. Experimental results show that the proposed method is robust to inaccuracies in point annotations and comparison with Hover-Net trained with fully annotated instance masks shows that a degradation in segmentation performance does not always imply a degradation in higher order tasks such as tissue classification.