L. Vaickus

DeltaAI: Semi-Autonomous Tissue Grossing Measurements and Recommendations using Neural Radiance Fields for Rapid, Complete Intraoperative Histological Assessment of Tumor Margins

DeltaAI uses Neural Radiance Fields to create 3D tissue models for faster and more accurate intraoperative tumor margin assessment in Mohs Micrographic Surgery.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining

This paper develops machine learning models to infer spatial transcriptomics data from H&E-stained skin slides, improving the assessment of photoaging and its therapeutic implications.

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis

This paper applies spatial transcriptomics data with contrastive crossmodal pretraining to improve graph-based deep learning models for cancer histopathology analysis.

Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

This study investigates using deep learning to predict spatial transcriptomics data from whole slide images to assess colorectal tumor metastasis, finding that convolutional networks outperform transformer and graph-based models.

Assessment of Emerging Pretraining Strategies in Interpretable Multimodal Deep Learning for Cancer Prognostication

This study evaluates pretraining strategies in an interpretable multimodal deep learning framework for cancer prognosis, showing that pretraining improves performance over standard methods.

Graph neural networks ameliorate potential impacts of imprecise large-scale autonomous immunofluorescence labeling of immune cells on whole slide images

This study uses graph neural networks to improve TIL classification on whole slide images by leveraging contextual features from immunofluorescence labeling.

Estimating the Inter- and Intra-Rater Reliability for NASH Fibrosis Staging in the Presence of Bridge Ordinal Ratings with Hierarchical Bridge Category Models

This paper develops hierarchical Bayesian models to assess inter- and intra-rater reliability for NASH fibrosis staging, showing improved precision over ad hoc methods.

Bridge Category Models: Development of Bayesian Modelling Procedures to Account for Bridge Ordinal Ratings for Disease Staging

The paper develops Bayesian models to handle bridge ordinal ratings in disease staging and shows they reduce bias and precision loss in models.

Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks

The paper introduces a framework combining graph neural networks and topological data analysis to extract and visualize features from whole-slide images, demonstrating its application in colon cancer staging.