Graph Perceiver Network for Lung Tumor and Bronchial Premalignant Lesion Stratification from Histopathology.

Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous cell carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. This study followed a novel computational approach, the Graph Perceiver Network, leveraging hematoxylin and eosin-stained whole slide images to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues. The Graph Perceiver Network outperformed existing frameworks in classification accuracy predicting LUSC, lung adenocarcinoma, and nontumor lung tissue on The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium datasets containing lung resection tissues while efficiently generating pathologist-aligned, class-specific heatmaps.

A Graph-Transformer for Whole Slide Image Classification.

Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as the WSI and neglect overall WSI-level information that is significant in disease grading.

OCT angiography fractal analysis-based quantification of macular vascular density in branch retinal vein occlusion eyes.

Objective: To compare the macular vascular density in branch retinal vein occlusion (BRVO) versus normals on optical coherence tomography angiography (OCTA) using fractal analysis and to describe the factors bearing on final visual outcomes.

Participants: This study is a retrospective observational comparative case series. Diagnosed cases of unilateral BRVO that underwent treatment and resolved were included.