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Found 4 papers, 3 papers with code
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images
Nevertheless, the effectiveness of CL is highly dependent on the quality of the positive and negative sample pairs.
Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
Hence, both modules establish a learnable connection to solve the semantic gaps between the encoder and the decoder, which leads to a high-performance segmentation model for medical images.
Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement
Most of the existing image enhancement methods mainly focus on improving the image quality by leveraging the guidance of high-quality images, which is difficult to be collected in medical applications.
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation.
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