6 papers
Robustness of transferability estimation metrics for medical imaging
Niclas Claßen, Théo Sourget, Dovile Juodelyte +2
In transfer learning, the choice of source model largely influences the performance on a target dataset. Still, selecting a fitting source remains a challenging task, especially in…
Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction
Yucheng Lu, Shunxin Wang, Dovile Juodelyte +1
In this paper, we explore how conventional image enhancement can improve model robustness in medical image analysis. By applying commonly used normalization methods to images from…
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Sarah de Boer +26
Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the gener…
Augmenting Chest X-ray Datasets with Non-Expert Annotations
Veronika Cheplygina, Cathrine Damgaard, Trine Naja Eriksen +2
The advancement of machine learning algorithms in medical image analysis requires the expansion of training datasets. A popular and cost-effective approach is automated annotation…
On dataset transferability in medical image classification
Dovile Juodelyte, Enzo Ferrante, Yucheng Lu +3
Current transferability estimation methods designed for natural image datasets are often suboptimal in medical image classification. These methods primarily focus on estimating the…
Copycats: the many lives of a publicly available medical imaging dataset
Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Dovile Juodelyte +5
Medical Imaging (MI) datasets are fundamental to artificial intelligence in healthcare. The accuracy, robustness, and fairness of diagnostic algorithms depend on the data (and its…