papers

Publications (18)

cs.CV2024

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…

cs.FL2025

Frequency Automata: A novel formal model of hybrid systems in combined time and frequency domains

Moon Kim, Avinash Malik, Partha Roop

Hybrid systems are mostly modelled, simulated, and verified in the time domain by computer scientists. Engineers, however, use both frequency and time domain modelling due to their…

eess.IV2023

Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images

Matthias Hadlich, Zdravko Marinov, Moon Kim +3

Deep learning has revolutionized the accurate segmentation of diseases in medical imaging. However, achieving such results requires training with numerous manual voxel annotations.…

eess.IV2024

Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methods

Zdravko Marinov, Moon Kim, Jens Kleesiek +1

Interactive segmentation plays a crucial role in accelerating the annotation, particularly in domains requiring specialized expertise such as nuclear medicine. For example, annotat…

cs.AI2026

Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why

Osman Alperen Çinar-Koraş, Marie Bauer, Sameh Khattab +7

Patient contexts span hundreds of heterogeneous documents and thousands of structured data points, yet the document-level metadata that AI systems need for retrieval and triage is…

cs.HC2025

Beyond the Desktop: XR-Driven Segmentation with Meta Quest 3 and MX Ink

Lisle Faray de Paiva, Gijs Luijten, Ana Sofia Ferreira Santos +4

Medical imaging segmentation is essential in clinical settings for diagnosing diseases, planning surgeries, and other procedures. However, manual annotation is a cumbersome and eff…

cond-mat.mes-hall2019

Formation of Graphene atop a Si adlayer on the C-face of SiC

Jun Li, Qingxiao Wang, Guowei He +6

The structure of the SiC(000-1) surface, the C-face of the {0001} SiC surfaces, is studied as a function of temperature and of pressure in a gaseous environment of disilane (Si2H6)…

cs.CV2024

Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain

Markus R. Bujotzek, Ünal Akünal, Stefan Denner +17

Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments…

cond-mat.str-el2026

Non-local low energy neutral excitations in a strongly disordered triangular Mott magnet CrSeBr

Wenhao Liu, Dechen Zhang, Yuanqi Lyu +13

Understanding if low-energy excitations can remain itinerant in the presence of strong disorder remains a central challenge in frustrated quantum magnets, where disorder is general…

cs.LG2024

ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics

Oishi Banerjee, Agustina Saenz, Kay Wu +17

Given the rapidly expanding capabilities of generative AI models for radiology, there is a need for robust metrics that can accurately measure the quality of AI-generated radiology…

cs.CV2025

Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries

Frederic Jonske, Moon Kim, Enrico Nasca +8

It is an open secret that ImageNet is treated as the panacea of pretraining. Particularly in medical machine learning, models not trained from scratch are often finetuned based on…

cs.CV2024

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…

eess.IV2023

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…

cond-mat.supr-con2019

Spacing Dependent and Doping Independent Superconductivity in Intercalated 1T Two Dimensional SnSe2

Hanlin Wu, Sheng Li, Michael Susner +4

The weak van der Waals interlayer interactions in the transition metal dichalcogenide (TMD) materials have created a rich platform to study their exotic electronic properties throu…

cs.CV2023

Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues

Verena Jasmin Hallitschke, Tobias Schlumberger, Philipp Kataliakos +6

Recently, deep learning enabled the accurate segmentation of various diseases in medical imaging. These performances, however, typically demand large amounts of manual voxel annota…

cs.CY2025

Regulating radiology AI medical devices that evolve in their lifecycle

Camila González, Moritz Fuchs, Daniel Pinto dos Santos +9

Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiolo…

cs.CL2024

Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!

Mariana Lindo, Ana Sofia Santos, André Ferreira +10

The impression section of a radiology report summarizes important radiology findings and plays a critical role in communicating these findings to physicians. However, the preparati…

cond-mat.supr-con2024

Solid-State Reactions at Niobium-Germanium Interfaces in Hybrid Superconductor-Semiconductor Devices

Bernardo Langa, Deepak Sapkota, Ivan Lainez +8

Hybrid Superconductor-Semiconductor (S-Sm) materials systems are promising candidates for quantum computing applications. Their integration into superconducting electronics has ena…