15.4k citations · 15.6k across the 14 of their papers we have counts for
14 papers
MV-MLM: Bridging Multi-View Mammography and Language for Breast Cancer Diagnosis and Risk Prediction
Shunjie-Fabian Zheng, Hyeonjun Lee, Thijs Kooi +1
Large annotated datasets are essential for training robust Computer-Aided Diagnosis (CAD) models for breast cancer detection or risk prediction. However, acquiring such datasets wi…
Breast Cancer VLMs: Clinically Practical Vision-Language Train-Inference Models
Shunjie-Fabian Zheng, Hyeonjun Lee, Thijs Kooi +1
Breast cancer remains the most commonly diagnosed malignancy among women in the developed world. Early detection through mammography screening plays a pivotal role in reducing mort…
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…
Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains
Samia Belhadj, Sanguk Park, Ambika Seth +2
Fairness in medical AI is increasingly recognized as a crucial aspect of healthcare delivery. While most of the prior work done on fairness emphasizes the importance of equal perfo…
SelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labeling
Laurent Dillard, Hyeonsoo Lee, Weonsuk Lee +3
When developing Computer Aided Detection (CAD) systems for Digital Breast Tomosynthesis (DBT), the complexity arising from the volumetric nature of the modality poses significant t…
Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data
Junha Song, Tae Soo Kim, Junha Kim +3
This paper aims to adapt the source model to the target environment, leveraging small user feedback (i.e., labeled target data) readily available in real-world applications. We fin…
ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity
Sumin Seo, JaeWoong Shin, Jaewoo Kang +2
Deep learning has shown great potential in assisting radiologists in reading chest X-ray (CXR) images, but its need for expensive annotations for improving performance prevents wid…
Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images
Hyeonsoo Lee, Junha Kim, Eunkyung Park +3
Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the…
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…
OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs
Gunhee Nam, Taesoo Kim, Sanghyup Lee +1
The accurate localization of inserted medical tubes and parts of human anatomy is a common problem when analyzing chest radiographs and something deep neural networks could potenti…
Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs
Tae Soo Kim, Geonwoon Jang, Sanghyup Lee +1
As deep networks require large amounts of accurately labeled training data, a strategy to collect sufficiently large and accurate annotations is as important as innovations in reco…
Tutorial on the development of AI models for medical image analysis
Thijs Kooi
The idea of using computers to read medical scans was introduced as early as 1966. However, limits to machine learning technology meant progress was slow initially. The Alexnet bre…
Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks
Thijs Kooi, Nico Karssemeijer
We investigate the addition of symmetry and temporal context information to a deep Convolutional Neural Network (CNN) with the purpose of detecting malignant soft tissue lesions in…
A Survey on Deep Learning in Medical Image Analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi +6
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning…