35 citations · 41 across the 18 of their papers we have counts for
23 papers
BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation
Luc Bouteille, Frederic Jonske, Jens Kleesiek +1
Common segmentation losses aggregate errors voxel-wise, so lesions influence the objective in proportion to their volume, giving small but clinically critical lesions disproportion…
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…
Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks
Pradnya Halady, Jiale Wei, Zdravko Marinov +2
Visual in-context learning has been proposed as a pathway towards dynamic models that can generate predictions based on a provided context and thereby can adapt to new vision tasks…
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…
IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning
Qian Yin, Di Wen, Kunyu Peng +11
Dense temporal annotation of procedural activity videos is vital for action understanding and embodied intelligence but remains labor-intensive due to reactive tools. Each correcti…
IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction
Haoshen Zhang, Di Wen, Kunyu Peng +12
We present IMPACT-HOI, a mixed-initiative framework for annotating egocentric procedural video by constructing structured event graphs for Human-Object Interactions (HOI), motivate…
IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory
Weitong Kong, Di Wen, Kunyu Peng +10
Correcting errors in long-video understanding is disproportionately costly: existing multimodal pipelines produce opaque, end-to-end outputs that expose no intermediate state for i…
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…
Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations
David Schneider, Simon Reiß, Marco Kugler +6
Exploring the intricate dynamics between muscular and skeletal structures is pivotal for understanding human motion. This domain presents substantial challenges, primarily attribut…
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…
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…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
Alexander Jaus, Constantin Seibold, Kelsey Hermann +5
In this study, we present a method for generating automated anatomy segmentation datasets using a sequential process that involves nnU-Net-based pseudo-labeling and anatomy-guided…
Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling
Constantin Seibold, Alexander Jaus, Matthias A. Fink +5
Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we prop…
Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning
Alexander Jaus, Kailun Yang, Rainer Stiefelhagen
In this work, we introduce panoramic panoptic segmentation, as the most holistic scene understanding, both in terms of Field of View (FoV) and image-level understanding for standar…
Panoramic Panoptic Segmentation: Towards Complete Surrounding Understanding via Unsupervised Contrastive Learning
Alexander Jaus, Kailun Yang, Rainer Stiefelhagen
In this work, we introduce panoramic panoptic segmentation as the most holistic scene understanding both in terms of field of view and image level understanding for standard camera…