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Found 4 papers, 2 papers with code
RTA-Former: Reverse Transformer Attention for Polyp Segmentation
Polyp segmentation is a key aspect of colorectal cancer prevention, enabling early detection and guiding subsequent treatments.
Evidential Uncertainty Quantification: A Variance-Based Perspective
Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning.
Automated Artifact Detection in Ultra-widefield Fundus Photography of Patients with Sickle Cell Disease
A pre-trained ResNet-50 neural network was trained on a subset of the images and the classification accuracy, sensitivity, and specificity were quantified on the hold out test set.
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization
We investigated uncertainty sampling, annotation redundancy restriction, and initial dataset selection techniques.
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