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Found 6 papers, 3 papers with code
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays
Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent.
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning
Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medical imaging field.
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring
We show that our sequential mistrust scores achieve high drift detection rates; over 90% of the streams show < 20% error for all domains.
TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring
We show that TIME-LAPSE is more driven by semantic content compared to other methods, i. e., it is more robust to dataset statistics.
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels
Optimization plays a key role in the training of deep neural networks.
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times.
