758 citations · 2.1k across the 52 of their papers we have counts for
41 papers · 1 filter
Medical Imaging AI Competitions Lack Fairness
Annika Reinke, Evangelia Christodoulou, Sthuthi Sadananda +34
Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shaping methodological progress. How…
Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification
Yucheng Lu, Hubert Dariusz Zając, Veronika Cheplygina +1
Transfer learning is crucial for medical imaging, yet the selection of source datasets often relies on researchers' intuition rather than systematic principles, which can impact th…
False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims
Evangelia Christodoulou, Annika Reinke, Pascaline Andrè +23
Performance comparisons are fundamental in medical imaging Artificial Intelligence (AI) research, often driving claims of superiority based on relative improvements in common perfo…
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…
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…
SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild
Jannik Elsäßer, Laura Weihl, Veronika Cheplygina +1
Seagrass meadows play a crucial role in marine ecosystems, providing benefits such as carbon sequestration, water quality improvement, and habitat provision. Monitoring the distrib…
Mask of truth: model sensitivity to unexpected regions of medical images
Théo Sourget, Michelle Hestbek-Møller, Amelia Jiménez-Sánchez +2
The development of larger models for medical image analysis has led to increased performance. However, it also affected our ability to explain and validate model decisions. Models…
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?
Evangelia Christodoulou, Annika Reinke, Rola Houhou +19
Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently…
Exploring connections of spectral analysis and transfer learning in medical imaging
Yucheng Lu, Dovile Juodelyte, Jonathan D. Victor +1
In this paper, we use spectral analysis to investigate transfer learning and study model sensitivity to frequency shortcuts in medical imaging. By analyzing the power spectrum dens…
Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging
Dovile Juodelyte, Yucheng Lu, Amelia Jiménez-Sánchez +3
Transfer learning has become an essential part of medical imaging classification algorithms, often leveraging ImageNet weights. The domain shift from natural to medical images has…
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…
[Citation needed] Data usage and citation practices in medical imaging conferences
Théo Sourget, Ahmet Akkoç, Stinna Winther +5
Medical imaging papers often focus on methodology, but the quality of the algorithms and the validity of the conclusions are highly dependent on the datasets used. As creating data…
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…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Revisiting Hidden Representations in Transfer Learning for Medical Imaging
Dovile Juodelyte, Amelia Jiménez-Sánchez, Veronika Cheplygina
While a key component to the success of deep learning is the availability of massive amounts of training data, medical image datasets are often limited in diversity and size. Trans…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays
Amelia Jiménez-Sánchez, Dovile Juodelyte, Bethany Chamberlain +1
The availability of large public datasets and the increased amount of computing power have shifted the interest of the medical community to high-performance algorithms. However, li…
Detection of Furigana Text in Images
Nikolaj Kjøller Bjerregaard, Veronika Cheplygina, Stefan Heinrich
Furigana are pronunciation notes used in Japanese writing. Being able to detect these can help improve optical character recognition (OCR) performance or make more accurate digital…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification
Irma van den Brandt, Floris Fok, Bas Mulders +2
Transfer learning is a commonly used strategy for medical image classification, especially via pretraining on source data and fine-tuning on target data. There is currently no cons…
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification
Ralf Raumanns, Gerard Schouten, Max Joosten +2
We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visu…
High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey
Rosana El Jurdi, Caroline Petitjean, Paul Honeine +2
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tas…