16 citations · 16 across the 8 of their papers we have counts for
9 papers · 1 filter
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…
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…