6 citations · 7 across the 2 of their papers we have counts for
4 papers
Deep Learning-Based Fixation Type Prediction for Quality Assurance in Digital Pathology
Oskar Thaeter, Tanja Niedermair, Jan E. G. Albin +3
Accurate annotation of fixation type is a critical step in slide preparation for pathology laboratories. However, this manual process is prone to errors, impacting downstream analy…
A Multicenter Benchmark of Multiple Instance Learning Models for Lymphoma Subtyping from HE-stained Whole Slide Images
Rao Muhammad Umer, Daniel Sens, Jonathan Noll +12
Timely and accurate lymphoma diagnosis is essential for guiding cancer treatment. Standard diagnostic practice combines hematoxylin and eosin (HE)-stained whole slide images with i…
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Dominik Müller, Philip Meyer, Lukas Rentschler +10
Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11…
DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks
Dominik Müller, Philip Meyer, Lukas Rentschler +8
Advances in digital pathology and artificial intelligence (AI) offer promising opportunities for clinical decision support and enhancing diagnostic workflows. Previous studies alre…