A vision–language foundation model for the generation of realistic chest x-ray images
The paucity of high-quality medical imaging datasets could be mitigated by machine
learning models that generate compositionally diverse images that faithfully represent …
learning models that generate compositionally diverse images that faithfully represent …
Imitate: Clinical prior guided hierarchical vision-language pre-training
In the field of medical Vision-Language Pretraining (VLP), significant efforts have been
devoted to deriving text and image features from both clinical reports and associated …
devoted to deriving text and image features from both clinical reports and associated …
Utilizing synthetic data for medical vision-language pre-training: Bypassing the need for real images
Medical Vision-Language Pre-training (VLP) learns representations jointly from medical
images and paired radiology reports. It typically requires large-scale paired image-text …
images and paired radiology reports. It typically requires large-scale paired image-text …
Foundation Models in Radiology: What, How, When, Why and Why Not
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep
learning models capable of interpreting and generating both textual and imaging data. Such …
learning models capable of interpreting and generating both textual and imaging data. Such …
Multimodal masked siamese network improves chest X-ray representation learning
Self-supervised learning methods for medical images primarily rely on the imaging modality
during pretraining. Although such approaches deliver promising results, they do not take …
during pretraining. Although such approaches deliver promising results, they do not take …
Leveraging Multi-Annotator Label Uncertainties as Privileged Information for Acute Respiratory Distress Syndrome Detection in Chest X-ray Images
Acute Respiratory Distress Syndrome (ARDS) is a life-threatening lung injury for which early
diagnosis and evidence-based treatment can improve patient outcomes. Chest X-rays …
diagnosis and evidence-based treatment can improve patient outcomes. Chest X-rays …
Contrastive learning with consistent representations
Contrastive learning demonstrates great promise for representation learning. Data
augmentations play a critical role in contrastive learning by providing informative views of …
augmentations play a critical role in contrastive learning by providing informative views of …
DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery
Self-supervised learning (SSL) has potential for effective representation learning in medical
imaging, but the choice of data augmentation is critical and domain-specific. It remains …
imaging, but the choice of data augmentation is critical and domain-specific. It remains …
[PDF][PDF] Laparoflow-SSL: Image analysis from a tiny dataset through self-supervised transformers leveraging unlabeled surgical video
K Moens, J De Vylder, M Blaschko… - … of Machine Learning …, 2024 - lirias.kuleuven.be
During minimally invasive surgery, surgeons monitor their actions and the relevant tissue
through a camera. This provides an ideal environment for artificial intelligence (AI) assisted …
through a camera. This provides an ideal environment for artificial intelligence (AI) assisted …
Structured Model Pruning for Efficient Inference in Computational Pathology
Recent years have seen significant efforts to adopt Artificial Intelligence (AI) in healthcare for
various use cases, from computer-aided diagnosis to ICU triage. However, the size of AI …
various use cases, from computer-aided diagnosis to ICU triage. However, the size of AI …