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20212025
most citedThe Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

60 citations

7 papers

cs.AI2025

Evaluating Compliance with Visualization Guidelines in Diagrams for Scientific Publications Using Large Vision Language Models

Johannes Rückert, Louise Bloch, Christoph M. Friedrich

Diagrams are widely used to visualize data in publications. The research field of data visualization deals with defining principles and guidelines for the creation and use of these…

eess.IV2024★ 55 cited

ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset

Johannes Rückert, Louise Bloch, Raphael Brüngel +11

Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper intro…

cs.DL2023★ 1 cited

PreprintResolver: Improving Citation Quality by Resolving Published Versions of ArXiv Preprints using Literature Databases

Louise Bloch, Johannes Rückert, Christoph M. Friedrich

The growing impact of preprint servers enables the rapid sharing of time-sensitive research. Likewise, it is becoming increasingly difficult to distinguish high-quality, peer-revie…

q-bio.OT2023★ 60 cited

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff +111

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is mean…

cs.CL2022★ 8 cited

Domain Adaptation of Transformer-Based Models using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback

Ahmad Idrissi-Yaghir, Henning Schäfer, Nadja Bauer +1

Understanding customer feedback is becoming a necessity for companies to identify problems and improve their products and services. Text classification and sentiment analysis can p…

cs.LG2022★ 27 cited

Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets

Louise Bloch, Christoph M. Friedrich

Purpose: Hard-to-interpret Black-box Machine Learning (ML) were often used for early Alzheimer's Disease (AD) detection. Methods: To interpret eXtreme Gradient Boosting (XGBoost),…