1 citations · 2 across the 11 of their papers we have counts for
15 papers
Case2Flow: Bridging Patient Cases and Guideline Flowcharts through Multimodal Retrieval
Jiale Wei, Yufan Chen, Alexander Jaus +5
Medical guidelines encode rich, evidence-based decision logic, yet the specific decision artifact a clinician needs is hard to locate within a guideline, let alone across guideline…
The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
Jakob Dexl, Katharina Jeblick, Andreas Mittermeier +27
We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body PET/CT under a compositional generaliza…
SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery
Valentin Wagner, Sebastian Bullinger, Michael Arens +1
We present SatGeo-NeRF, a geometrically regularized NeRF for satellite imagery that mitigates overfitting-induced geometric artifacts observed in current state-of-the-art models us…
Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation
Luc Bouteille, Alexander Jaus, Jens Kleesiek +2
Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall…
GRASPing Anatomy to Improve Pathology Segmentation
Keyi Li, Alexander Jaus, Jens Kleesiek +1
Radiologists rely on anatomical understanding to accurately delineate pathologies, yet most current deep learning approaches use pure pattern recognition and ignore the anatomical…
Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement
Cedric Zöllner, Simon Reiß, Alexander Jaus +3
When preoperative planning for surgeries is conducted on the basis of medical images, artificial intelligence methods can support medical doctors during assessment. In this work, w…
Is Visual in-Context Learning for Compositional Medical Tasks within Reach?
Simon Reiß, Zdravko Marinov, Alexander Jaus +4
In this paper, we explore the potential of visual in-context learning to enable a single model to handle multiple tasks and adapt to new tasks during test time without re-training.…
Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets
Alexander Jaus, Zdravko Marinov, Constantin Seibold +4
Manually refining radiological segmentation masks is highly resource-intensive. To determine when this expert commitment is truly justified for the training of segmentation models,…
OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection
David Schneider, Zdravko Marinov, Moritz Mistol +6
Visual fall detection models are usually trained on small, staged datasets. Their real-world utility remains unclear; such data lacks diversity and evaluation protocols differ from…
Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks
Alexander Jaus, Constantin Seibold, Simon Reiß +6
We present Connected-Component~(CC)-Metrics, a novel semantic segmentation evaluation protocol, targeted to align existing semantic segmentation metrics to a multi-instance detecti…
LIMIS: Towards Language-based Interactive Medical Image Segmentation
Lena Heinemann, Alexander Jaus, Zdravko Marinov +4
Within this work, we introduce LIMIS: The first purely language-based interactive medical image segmentation model. We achieve this by adapting Grounded SAM to the medical domain a…
Statewide Visual Geolocalization in the Wild
Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner +2
This work presents a method that is able to predict the geolocation of a street-view photo taken in the wild within a state-sized search region by matching against a database of ae…
Data Diet: Can Trimming PET/CT Datasets Enhance Lesion Segmentation?
Alexander Jaus, Simon Reiß, Jens Kleesiek +1
In this work, we describe our approach to compete in the autoPET3 datacentric track. While conventional wisdom suggests that larger datasets lead to better model performance, recen…
Anatomy-guided Pathology Segmentation
Alexander Jaus, Constantin Seibold, Simon Reiß +7
Pathological structures in medical images are typically deviations from the expected anatomy of a patient. While clinicians consider this interplay between anatomy and pathology, r…
SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading
Tu Anh Dinh, Carlos Mullov, Leonard Bärmann +15
With the rapid development of Large Language Models (LLMs), it is crucial to have benchmarks which can evaluate the ability of LLMs on different domains. One common use of LLMs is…