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Found 5 papers, 1 papers with code
Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction
This work focuses on leveraging 'off-the-shelf' pre-trained models as deep feature extractors for scoring disease severity with minimal training time.
Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration
Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes.
Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data
This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available.
Lung Ultrasound Segmentation and Adaptation between COVID-19 and Community-Acquired Pneumonia
Furthermore, we offer a possible explanation that correlates the segmentation performance to label consistency and data domain diversity in this point-of-care lung ultrasound application.
Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels
When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels.
