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20242026
most citedGraph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?

2 citations · 3 across the 10 of their papers we have counts for

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cs.CV2026

Cross-Modal MRI Ovary Segmentation in Endometriosis Using Unpaired TVUS Prototype Priors

Xingjian Kang, Lina Felsner, Dominik Perrin +5

Transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) provide complementary information for endometriosis image analysis, yet existing studies mainly focus on single-…

cs.CV2025

Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation

Stefan M. Fischer, Johannes Kiechle, Laura Daza +6

In this work, we introduce Progressive Growing of Patch Size, an automatic curriculum learning approach for 3D medical image segmentation. Our approach progressively increases the…

cs.CV2024★ 2 cited

Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?

Johannes Kiechle, Daniel M. Lang, Stefan M. Fischer +3

Recent studies have underscored the capabilities of natural imaging foundation models to serve as powerful feature extractors, even in a zero-shot setting for medical imaging data.…

cs.CV2024

Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks

Stefan M. Fischer, Lina Felsner, Richard Osuala +4

In this work, we introduce Progressive Growing of Patch Size, a resource-efficient implicit curriculum learning approach for dense prediction tasks. Our curriculum approach is defi…

cs.CV2024

Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration

Anna Reithmeir, Lina Felsner, Rickmer Braren +2

Physics-inspired regularization is desired for intra-patient image registration since it can effectively capture the biomechanical characteristics of anatomical structures. However…

cs.CV2024

Language Models Meet Anomaly Detection for Better Interpretability and Generalizability

Jun Li, Su Hwan Kim, Philip Müller +5

This research explores the integration of language models and unsupervised anomaly detection in medical imaging, addressing two key questions: (1) Can language models enhance the i…