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Found 3 papers, 3 papers with code
Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization
Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface.
Histopathological Image Classification
Histopathological Segmentation
+5
Deep Learning Generates Synthetic Cancer Histology for Explainability and Education
Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists.
Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology
Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.
