Search Results for author:
Found 7 papers, 4 papers with code
Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain
Our results underscore the value of efforts needed to translate FL into real-world applications by demonstrating advantageous performance over alternatives, and emphasize the importance of strategic organization, robust management of distributed data and infrastructure in real-world settings.
Automatic classification of prostate MR series type using image content and metadata
With the wealth of medical image data, efficient curation is essential.
Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation
We relate this shortcoming to two major issues: 1) the complex nature of many methods which deeply ties them to the underlying segmentation model, thus preventing a migration to more powerful state-of-the-art models as the field progresses and 2) the lack of a systematic evaluation to validate consistent performance across the broader medical domain, resulting in a lack of trust when applying these methods to new segmentation problems.
Leveraging Foundation Models for Content-Based Medical Image Retrieval in Radiology
Despite these challenges, our research underscores the vast potential of foundation models for CBIR in radiology, proposing a shift towards versatile, general-purpose medical image retrieval systems that do not require specific tuning.
Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy.
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI.
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation.
