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Found 8 papers, 7 papers with code
Bridging Generalization Gaps in High Content Imaging Through Online Self-Supervised Domain Adaptation
CODA can be applied to new, unlabeled out-of-domain data sources of different sizes, from a single plate to multiple experimental batches.
Are Natural Domain Foundation Models Useful for Medical Image Classification?
The deep learning field is converging towards the use of general foundation models that can be easily adapted for diverse tasks.
Pretrained ViTs Yield Versatile Representations For Medical Images
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis, pushing the state-of-the-art in classification, detection and segmentation tasks.
Metadata-guided Consistency Learning for High Content Images
High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs.
What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors
Transfer learning is a standard technique to transfer knowledge from one domain to another.
Should we Replace CNNs with Transformers for Medical Images?
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis, pushing the state-of-the-art in classification, detection and segmentation tasks.
Is it Time to Replace CNNs with Transformers for Medical Images?
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis.
Adding Seemingly Uninformative Labels Helps in Low Data Regimes
Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features.
