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Found 5 papers, 1 papers with code
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos
Self-supervised learning (SSL) methods have shown promise for medical imaging applications by learning meaningful visual representations, even when the amount of labeled data is limited.
How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound
Adding synthetic training data using generative models offers a low-cost method to deal effectively with the data scarcity challenge, and can also address data imbalance and patient privacy issues.
Weakly Semi-Supervised Detection in Lung Ultrasound Videos
Frame-by-frame annotation of bounding boxes by clinical experts is often required to train fully supervised object detection models on medical video data.
Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information
Malaria is a life-threatening disease affecting millions.
Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy
To explore this potential resource, we develop a hybrid classifier (Softmax-Pooling Hybrid, $SPH$) that uses Softmax on high-scoring samples, but on low-scoring samples uses a log-likelihood method that pools the information from the full array $D$.
