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Found 7 papers, 2 papers with code
Synthetic Augmentation with Large-scale Unconditional Pre-training
To further reduce the dependency on annotated data, we propose a synthetic augmentation method called HistoDiffusion, which can be pre-trained on large-scale unlabeled datasets and later applied to a small-scale labeled dataset for augmented training.
Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning
We present such a machine learning approach for analyzing Raman spectra of human and avian viruses.
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
Histopathological analysis is the present gold standard for precancerous lesion diagnosis.
A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis
Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks.
Synthetic Sample Selection via Reinforcement Learning
However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images.
Selective Synthetic Augmentation with Quality Assurance
To mitigate these issues, we investigate a novel data augmentation pipeline that selectively adds new synthetic images generated by conditional Adversarial Networks (cGANs), rather than extending directly the training set with synthetic images.
Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification
Our models are evaluated on a cervical histopathology image dataset with a limited number of patch-level CIN grade annotations.
