Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

Nuclear-derived morphological features and biomarkers provide relevant insights regarding
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …

The successful integration of neural networks in a clinical setting is still uncommon despite
major successes achieved by artificial intelligence in other domains. This is mainly due to …

Semantic segmentation is a fundamental task in computer vision and finds extensive
applications in scene understanding, medical image analysis, and remote sensing. With the …

In medical imaging, semantic segmentation is crucial for accurate diagnosis. However, it is
constrained by the scarcity of labeled data. To reduce the dependency on extensive …

Tissue semantic segmentation is one of the key tasks in computational pathology. To avoid
the expensive and laborious acquisition of pixel-level annotations, a wide range of studies …

Histopathology image classification is one of the most important fundamental tasks in the
automation analysis of whole slide imaging and is essential for computer-aided pathological …

Graph Collaborative Filtering is a widely adopted approach for recommendation, which
captures similar behavior features through graph neural network. Recently, Contrastive …

Tissue semantic segmentation from Hematoxylin and Eosin (H&E)-stained Whole Slide
Images (WSIs) is a highly effective technique in medical image analysis that has significantly …

Annotating tissue regions within whole-slide histology images poses a significant challenge
for clinical experts and practitioners. In this work, we propose the SPADESegResNet model …