Michael Vössing — accepted AI-conference papers

2024

Redefining the Laparoscopic Spatial Sense: AI-Based Intra- and Postoperative Measurement from Stereoimages

Leopold Müller, Patrick Hemmer, Moritz Queisner, Igor Sauer, Simeon Allmendinger, Johannes Jakubik +2

AAAI 2024technical

A significant challenge in image-guided surgery is the accurate measurement task of relevant structures such as vessel segments, resection margins, or bowel lengths. While this task is an essential component of many surgeries, it involves substantial human effort and is prone to inaccuracies. In thi…

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2023

Learning to Defer with Limited Expert Predictions

Patrick Hemmer, Lukas Thede, Michael Vössing, Johannes Jakubik, Niklas Kühl

AAAI 2023technical

Recent research suggests that combining AI models with a human expert can exceed the performance of either alone. The combination of their capabilities is often realized by learning to defer algorithms that enable the AI to learn to decide whether to make a prediction for a particular instance or de…

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2023

What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation

Benedikt Blumenstiel, Johannes Jakubik, Hilde Kuehne, Michael Vössing

NeurIPS 2023poster

While semantic segmentation has seen tremendous improvements in the past, there are still significant labeling efforts necessary and the problem of limited generalization to classes that have not been present during training. To address this problem, zero-shot semantic segmentation makes use of larg…

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2022

Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts

Patrick Hemmer, Sebastian Schellhammer, Michael Vössing, Johannes Jakubik, Gerhard Satzger

IJCAI 2022poster

Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts. In this context, it can be advantageous to defer certain instances to a single human expert when they are difficult to predict for the ML model. While previous work…

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