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Found 7 papers, 3 papers with code
Step-Calibrated Diffusion for Biomedical Optical Image Restoration
Here, we present Restorative Step-Calibrated Diffusion (RSCD), an unpaired image restoration method that views the image restoration problem as completing the finishing steps of a diffusion-based image generation task.
A self-supervised framework for learning whole slide representations
Self-supervised representation learning can achieve high-quality WSI visual feature learning for downstream diagnostic tasks, such as cancer diagnosis or molecular genetic prediction.
Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters
SpinePose accurately predicted spinopelvic parameters with excellent reliability comparable to fellowship-trained spine surgeons and neuroradiologists.
Fine-grained Text Style Transfer with Diffusion-Based Language Models
Diffusion probabilistic models have shown great success in generating high-quality images controllably, and researchers have tried to utilize this controllability into text generation domain.
Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging
Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment.
Hierarchical discriminative learning improves visual representations of biomedical microscopy
Importantly, sampled patches from WSIs of a patient's tumor are a diverse set of image examples that capture the same underlying cancer diagnosis.
OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology
Here, we present OpenSRH, the first public dataset of clinical SRH images from 300+ brain tumors patients and 1300+ unique whole slide optical images.
