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Found 5 papers, 1 papers with code
Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations
We test the effectiveness of our approach by evaluating building segmentation performance on test datasets with varying label fractions.
Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images
Like training in other modalities such as traditional endoscopy, CT, MRI, etc., a WCE training protocol would require an atlas comprising of a large corpora of images that show vivid descriptions of pathologies and abnormalities, ideally observed over a period of time.
This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopy
Due to the unequivocal need for understanding the decision processes of deep learning networks, both modal-dependent and model-agnostic techniques have become very popular.
From Labels to Priors in Capsule Endoscopy: A Prior Guided Approach for Improving Generalization with Few Labels
Prior works have considered using higher quality and quantity of labels as a way of tackling the lack of generalization, however this is hardly scalable considering pathology diversity not to mention that labeling large datasets encumbers the medical staff additionally.
Learning More for Free - A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy
The progress in Computer Aided Diagnosis (CADx) of Wireless Capsule Endoscopy (WCE) is thwarted by the lack of data.
