52 citations · 82 across the 6 of their papers we have counts for
7 papers
Autonomous Structuring of Radiology Reports Across Modalities at Archive Scale Using an Open-Weight Large Language Model
Friedrich Puttkammer, Fabian Drexel, Marlene Fritzsche +19
Purpose: To develop and evaluate an open-weight large language model (LLM) pipeline that converts an entire archive of free-text radiology reports into structured reports without h…
PARROT: An Open Multilingual Radiology Reports Dataset
Bastien Le Guellec, Kokou Adambounou, Lisa C Adams +85
Rationale and Objectives: To develop and validate PARROT (Polyglottal Annotated Radiology Reports for Open Testing), a large, multicentric, open-access dataset of fictional radiolo…
Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework
Sarah de Boer, Hartmut Häntze, Kiran Vaidhya Venkadesh +9
Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an importa…
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
Felix J. Dorfner, Janis L. Vahldiek, Leonhard Donle +15
Purpose: To examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression. Methods: This r…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
Hartmut Häntze, Lina Xu, Maximilian Rattunde +5
Computed tomography (CT) segmentation models often contain classes that are not currently supported by magnetic resonance imaging (MRI) segmentation models. In this study, we show…
MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain
Keno K. Bressem, Jens-Michalis Papaioannou, Paul Grundmann +12
This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million Germ…