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Found 3 papers, 2 papers with code
Promptable cancer segmentation using minimal expert-curated data
However, its adoption is limited by the high cost of expert annotations required for training and inter-observer variability in datasets.
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement.
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Based on the presented experiments from two clinical data sets, consisting of histopathology-labelled MR images from more than 800 and 500 patients in the respective UCLA and UCL PROMIS studies, we show that the proposed strategy can improve the diagnostic accuracy, by augmenting the radiologist reading of the MR imaging.
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