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Found 7 papers, 2 papers with code
Super-resolution of biomedical volumes with 2D supervision
Volumetric biomedical microscopy has the potential to increase the diagnostic information extracted from clinical tissue specimens and improve the diagnostic accuracy of both human pathologists and computational pathology models.
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.
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.
Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence
Conclusion: SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.
