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Found 3 papers, 1 papers with code
FP-PET: Large Model, Multiple Loss And Focused Practice
This study presents FP-PET, a comprehensive approach to medical image segmentation with a focus on CT and PET images.
Ultrasonic Image's Annotation Removal: A Self-supervised Noise2Noise Approach
Accurately annotated ultrasonic images are vital components of a high-quality medical report.
Label-noise-tolerant medical image classification via self-attention and self-supervised learning
Therefore, in this work, we innovatively devise noise-robust training approach to mitigate the adverse effects of noisy labels in medical image classification.
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