activity
20172026
most citedInterpretable Survival Prediction for Colorectal Cancer using Deep Learning

233 citations · 377 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

The System Hallucination Scale (SHS): A Minimal yet Effective Human-Centered Instrument for Evaluating Hallucination-Related Behavior in Large Language Models

Heimo Müller, Dominik Steiger, Markus Plass +1

We introduce the System Hallucination Scale (SHS), a lightweight and human-centered measurement instrument for assessing hallucination-related behavior in large language models (LL…

cs.CY2024★ 16 cited

Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative

Norman Zerbe, Lars Ole Schwen, Christian Geißler +17

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numero…

eess.IV2022★ 78 cited

Recommendations on test datasets for evaluating AI solutions in pathology

André Homeyer, Christian Geißler, Lars Ole Schwen +27

Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to…

cs.CV2020★ 50 cited

Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading

Ellery Wulczyn, Kunal Nagpal, Matthew Symonds +20

Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial i…

eess.IV2020★ 233 cited

Interpretable Survival Prediction for Colorectal Cancer using Deep Learning

Ellery Wulczyn, David F. Steiner, Melissa Moran +20

Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) f…

cs.AI2017

A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop

Andreas Holzinger, Markus Plass, Katharina Holzinger +3

The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressi…