Semantic segmentation, a fundamental research direction in synthetic aperture radar (SAR)
image interpretation, has significant application value for multiple sectors. However, noise …

Abstract Graph Neural Networks (GNNs) have emerged as a crucial deep learning
framework for graph-structured data. However, existing GNNs suffer from the scalability …

Structurally similar data exist in most practical semantic segmentation applications. For
example, objects can appear identical or positionally similar in many images, such as video …

Predicting labels of gigapixel whole-slide images (WSIs) and localizing regions of interest
(ROIs) with high precision are of great interest in computational pathology. The existing …

Attention mechanisms improve the performance of models by selectively processing
relevant information. However, existing attention mechanisms for CNNs do not utilize the …

Background and objectives: Graph neural network (GNN) has been extensively used in
histopathology whole slide image (WSI) analysis due to the efficiency and flexibility in …

Due to the coarse granularity of information extraction in image-level annotation-based
weakly supervised semantic segmentation algorithms, there exists a significant gap between …

Lesion segmentation is a fundamental task in medical image processing, often facing the
challenge of subtle lesions. It is important to detect these lesions, even though they can be …

In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to
various real-world challenges, with architectures such as Vision GNN (ViG) achieving state …

While standardized codecs like JPEG and HEVC-intra represent the industry standard in
image compression, neural Learned Image Compression (LIC) codecs represent a …