Area of research
Artificial Intelligence · Radiology, Nuclear Medicine and Imaging
Research interest
Research focused on Artificial intelligence and Normalization (sociology), with related work in Breast cancer, Image segmentation, H&E stain. Notable publications include 'A Multi-Organ Nucleus Segmentation Challenge', 'Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images', and 'Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images'.
The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue
Weakly supervised learning on unannotated H&E‐stained slides predicts <scp><i>BRAF</i></scp> mutation in thyroid cancer with high accuracy
Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images
A Multi-Organ Nucleus Segmentation Challenge
Quantification of intrinsic subtype ambiguity in Luminal A breast cancer and its relationship to clinical outcomes
Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering
Convolutional neural networks for prostate cancer recurrence prediction
Color normalization of histology slides using graph regularized sparse NMF
Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images