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Found 5 papers, 4 papers with code
Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation
Recent advancements in large foundation models have shown promising potential in the medical industry due to their flexible prompting capability.
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners.
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition
Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases.
Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images
We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI), a new method for training object detection models with noisy and missing annotations in a semi-supervised fashion.
Mammograms Classification: A Review
An advanced reliable low-cost form of screening method, Digital mammography has been used as an effective imaging method for breast cancer detection.
