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Found 5 papers, 3 papers with code
Critical Evaluation of Artificial Intelligence as Digital Twin of Pathologist for Prostate Cancer Pathology
Adjusting the decision threshold for the secondary Gleason pattern from 5% to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (kappa from 0. 44 to 0. 64).
Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence
Background: The clinical documentation of cystoscopy includes visual and textual materials.
CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AI
We use the CheXpert and PadChest public datasets to build and test a medical imaging AI drift monitoring workflow to track data and model drift without contemporaneous ground truth.
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes.
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation
Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem.
