6 citations · 10 across the 4 of their papers we have counts for
4 papers
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…
MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Data
Dennis Hartmann, Verena Schmid, Philip Meyer +3
Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms. Unfortunately, the current measures have their weaknesses wh…
MISeval: a Metric Library for Medical Image Segmentation Evaluation
Dominik Müller, Dennis Hartmann, Philip Meyer +3
Correct performance assessment is crucial for evaluating modern artificial intelligence algorithms in medicine like deep-learning based medical image segmentation models. However,…