Search Results for author:
Found 10 papers, 4 papers with code
RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision
We introduce RAD-DINO, a biomedical image encoder pre-trained solely on unimodal biomedical imaging data that obtains similar or greater performance than state-of-the-art biomedical language supervised models on a diverse range of benchmarks.
RadEdit: stress-testing biomedical vision models via diffusion image editing
Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing.
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images.
Combining Interventional and Observational Data Using Causal Reductions
Second, for continuous variables and assuming a linear-Gaussian model, we derive equality constraints for the parameters of the observational and interventional distributions.
Problems using deep generative models for probabilistic audio source separation
Recent advancements in deep generative modeling make it possible to learn prior distributions from complex data that subsequently can be used for Bayesian inference.
Selecting Data Augmentation for Simulating Interventions
We argue that causal concepts can be used to explain the success of data augmentation by describing how they can weaken the spurious correlation between the observed domains and the task labels.
DIVA: Domain Invariant Variational Autoencoders
We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain.
DIVA: Domain Invariant Variational Autoencoder
We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain.
Attention-based Deep Multiple Instance Learning
Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances.
Ranked #7 on
Aerial Scene Classification
on UCM (50% as trainset)
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification
The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field.
