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20182026
most citedMedical Image Retrieval Using Pretrained Embeddings

1 citations · 1 across the 7 of their papers we have counts for

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cs.CV2026

Zero-shot System for Automatic Body Region Detection for Volumetric CT and MR Images

Farnaz Khun Jush, Grit Werner, Mark Klemens +1

Reliable identification of anatomical body regions is a prerequisite for many automated medical imaging workflows, yet existing solutions remain heavily dependent on unreliable DIC…

cs.CV2025

Towards Selection of Large Multimodal Models as Engines for Burned-in Protected Health Information Detection in Medical Images

Tuan Truong, Guillermo Jimenez Perez, Pedro Osorio +1

The detection of Protected Health Information (PHI) in medical imaging is critical for safeguarding patient privacy and ensuring compliance with regulatory frameworks. Traditional…

cs.CV2025

Content-based 3D Image Retrieval and a ColBERT-inspired Re-ranking for Tumor Flagging and Staging

Farnaz Khun Jush, Steffen Vogler, Matthias Lenga

The increasing volume of medical images poses challenges for radiologists in retrieving relevant cases. Content-based image retrieval (CBIR) systems offer potential for efficient a…

cs.CV2025

Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images

Tuan Truong, Ivo M. Baltruschat, Mark Klemens +2

De-identification of medical images is a critical step to ensure privacy during data sharing in research and clinical settings. The initial step in this process involves detecting…

cs.CV2024

Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Study

Farnaz Khun Jush, Steffen Vogler, Tuan Truong +1

While content-based image retrieval (CBIR) has been extensively studied in natural image retrieval, its application to medical images presents ongoing challenges, primarily due to…

cs.CV2023

Benchmarking Pretrained Vision Embeddings for Near- and Duplicate Detection in Medical Images

Tuan Truong, Farnaz Khun Jush, Matthias Lenga

Near- and duplicate image detection is a critical concern in the field of medical imaging. Medical datasets often contain similar or duplicate images from various sources, which ca…