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Supplemental digital content reporting experimental framework, model architecture, training protocols, and evaluation of deep learning methods for improving biopsy decision-making in bi-parametric prostate MRI.
Year: 2023
Region / Institution: Unspecified medical research institution
Topic: Medical imaging, prostate cancer, MRI, machine learning
Document type: Supplemental Digital Content
Methodology: PI-RADS guided contrastive learning, convolutional neural networks, transformer encoders, ensemble models
Data: Bi-parametric prostate MR images, clinical variables including PSA, PSAD, age, and prostate volume
Audience: Radiologists, medical researchers, AI in healthcare professionals
Parameters: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), echo-planar imaging sequences, b-values 50, 1000, 1500 s/mm²
Model details: 36M parameter Representation Learner (RL) combining CNN and transformer encoders
Validation: t-SNE visualization, box and whisker plots, binary cross-entropy loss, early stopping, hyper-parameter tuning
Performance metrics: csPCa detection, avoidance of benign biopsies, ensemble prediction combining RL and clinical models
Training: 50 epochs for RL, 1000 epochs for biopsy decision models, AdamW optimizer, stochastic gradient descent with momentum, data augmentation including rotations, cropping, brightness/contrast adjustments, elastic deformations
Reference literature: Umapathy et al. 2023, Yala et al. 2021, Shen et al. 2023
Price: 8 / 10 USD
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