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eess.IV2025
"No negatives needed": weakly-supervised regression for interpretable tumor detection in whole-slide histopathology images
Marina D'Amato, Jeroen van der Laak, Francesco Ciompi
Accurate tumor detection in digital pathology whole-slide images (WSIs) is crucial for cancer diagnosis and treatment planning. Multiple Instance Learning (MIL) has emerged as a wi…
eess.IV2024
Benchmarking Hierarchical Image Pyramid Transformer for the classification of colon biopsies and polyps in histopathology images
Nohemi Sofia Leon Contreras, Marina D'Amato, Francesco Ciompi +5
Training neural networks with high-quality pixel-level annotation in histopathology whole-slide images (WSI) is an expensive process due to gigapixel resolution of WSIs. However, r…
eess.IV2023
HoVer-UNet: Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillation
Cristian Tommasino, Cristiano Russo, Antonio Maria Rinaldi +1
We present HoVer-UNet, an approach to distill the knowledge of the multi-branch HoVerNet framework for nuclei instance segmentation and classification in histopathology. We propose…