Institution profile

German Cancer Research Center

Academic institutioneurope · de
Official website
Research library137linked papers
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Selected work

Representative Papers

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

Jan 26, 2025

Automatic breast ultrasound (ABUS) tumor detection, segmentation, and classification are challenged by morphological heterogeneity, low signal-to-noise ratio, and scarcity of annotated 3D data. Method: We introduce the first publicly available, high-quality, multi-center ABUS tumor benchmark dataset and the TDSC-ABUS2023 international challenge platform—enabling the first unified three-task evaluation. Our proposed framework integrates multi-scale 3D CNNs, Transformers, semi-supervised learning, and boundary-aware loss to address ABUS-specific challenges including ill-defined tumor boundaries and low contrast. Contribution/Results: Our method achieves state-of-the-art performance: 82.3% mAP@0.5 for detection, 79.6% Dice for segmentation, and 91.4% accuracy for malignancy classification—significantly outperforming baselines. This work fills critical gaps in publicly accessible ABUS benchmarks and standardized multi-task evaluation, advancing intelligent early diagnosis of breast cancer.

2 citationsRead paper

A Convoy of Magnetic Millirobots Transports Endoscopic Instruments for Minimally-Invasive Surgery.

Jul 01, 2024Advancement of science

In minimally invasive surgery, microrobots suffer from insufficient traction on slippery, soft-tissue surfaces, hindering reliable transport of elongated instruments (e.g., endoscopes, catheters). To address this, we present TrainBot—a magnetically actuated millirobotic convoy system—where multiple millirobots cooperatively form a “train-like” configuration to enable stable, heavy-load instrument transport within narrow anatomical lumens (e.g., bile ducts, intestines). Key contributions include: (i) the first demonstration of millirobotic swarm-based cargo transport, achieving a twofold increase in output force; (ii) bioinspired, biocompatible microstructured feet that enhance individual propulsion force by threefold; and (iii) the world’s first millirobot-assisted electrodilatation procedure for biliary stricture relief. Integrated with wireless permanent-magnet actuation and multi-robot closed-loop control, TrainBot successfully validated biliary obstruction clearance, drainage tunnel creation, and targeted drug delivery in human-scale organ phantoms—significantly advancing precision instrument delivery in minimally invasive interventions.

2 citationsRead paper

ScaleMAI: Accelerating the Development of Trusted Datasets and AI Models

Jan 06, 2025

Medical AI development is hindered by lengthy dataset curation cycles and the decoupling of annotation from model training. To address this, we propose an AI-driven collaborative co-evolution framework for medical data, instantiated on pancreatic tumor CT analysis. Our approach introduces a novel human-in-the-loop, progressive “data flywheel” mechanism that jointly enhances annotation quality and model performance. Methodologically, it integrates multi-round human-in-the-loop iteration, 3D voxel-level semi-automatic annotation, domain-adaptive few-shot learning, and cross-task joint modeling (detection, segmentation, classification). We construct a high-quality, multi-task dataset comprising 25,362 CT scans. Our flagship model achieves annotation accuracy comparable to that of experts with 30 years of experience, delivering performance gains of 14%, 5%, and 72% over prior state-of-the-art on detection, segmentation, and classification benchmarks, respectively. This work transcends static dataset paradigms, enabling dynamic, scalable, and trustworthy medical AI infrastructure.

1 citationsRead paper
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