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Found 4 papers, 1 papers with code
A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound Segmentation
Conventional deep learning models deal with images one-by-one, requiring costly and time-consuming expert labeling in the field of medical imaging, and domain-specific restriction limits model generalizability.
Self-supervised TransUNet for Ultrasound regional segmentation of the distal radius in children
However, their performance highly relies on the quality and quantity of the data annotation.
Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels
This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations.
A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: Past, present and future
With more than 50 papers included in this review, we reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies and potential of computer-aided diagnosis to increase the impact of ultrasound applications on the future of thyroid cancer diagnosis.
