109 citations · 376 across the 17 of their papers we have counts for
18 papers
Atlas of MICCAI: 28 Years of Representation, Collaboration, and the Road to Inclusion
Amelia Jiménez-Sánchez, Laura Arbelaez Ossa, Joan Perramon-Llussà +6
Scientific knowledge in medical image analysis is shaped by venues such as the Medical Image Computing and Computer-Assisted Intervention (MICCAI) conference, which influence resea…
Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI
Joan Perramon-Llussà, Amelia Jiménez-Sánchez, Grzegorz Skorupko +4
Foundation models (FMs) show great promise for robust downstream performance across medical imaging tasks and modalities, including cardiac magnetic resonance (CMR), following task…
Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
Fangyijie Wang, Tanya Akumu, Vien Ngoc Dang +5
Foundation models promise to unify multiple clinical tasks within a single framework, but recent ultrasound studies report that unified models can underperform task-specific baseli…
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…
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…
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…
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…
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…
Memory-aware curriculum federated learning for breast cancer classification
Amelia Jiménez-Sánchez, Mickael Tardy, Miguel A. González Ballester +2
For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative sample…
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty
Amelia Jiménez-Sánchez, Diana Mateus, Sonja Kirchhoff +5
An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We rely on the commonly used AO sys…
Medical-based Deep Curriculum Learning for Improved Fracture Classification
Amelia Jiménez-Sánchez, Diana Mateus, Sonja Kirchhoff +5
Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge. In this work, we propose and compare several st…
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray ima…
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system. Although primarily dealing wi…
Capsule Networks against Medical Imaging Data Challenges
Amelia Jiménez-Sánchez, Shadi Albarqouni, Diana Mateus
A key component to the success of deep learning is the availability of massive amounts of training data. Building and annotating large datasets for solving medical image classifica…