7 citations · 9 across the 4 of their papers we have counts for
4 papers
DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
Carlijn Lems, Sander Moonemans, Natálie Klubíčková +53
Foundation models with visual question answering capabilities for digital pathology are emerging. Such unprecedented technology requires independent benchmarking to assess its pote…
KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images
Jiaqi Lv, Esha Sadia Nasir, Kesi Xu +4
Accurate detection and classification of nuclei in histopathology images are critical for diagnostic and research applications. We present KongNet, a multi-headed deep learning arc…
Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images
Jiaqi Lv, Yijie Zhu, Carmen Guadalupe Colin Tenorio +3
Melanoma is an aggressive form of skin cancer with rapid progression and high metastatic potential. Accurate characterisation of tissue morphology in melanoma is crucial for progno…
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling
Piotr Keller, Muhammad Dawood, Brinder Singh Chohan +1
Machine learning in computational pathology (CPath) often aggregates patch-level predictions from multi-gigapixel Whole Slide Images (WSIs) to generate WSI-level prediction scores…