radiology-deep-learning

radiology-deep-learning is a Computer vision skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-deep-learning D (68/100) and it passed the safety scan; the rating is based on static analysis only.

Design and audit imaging deep-learning studies to Radiology (RSNA) / CLAIM 2024 standard, or to Nature-portfolio / FUTURE-AI trustworthy-AI standard — architecture choice (2D/2.5D/3D CNN, Transformer/ViT, segmentation/detection nets, prognostic models), transfer learning vs self-supervised pretraining vs training from scratch, how images/masks/clinical/text/molecular inputs enter the model, data splitting and augmentation, class imbalance, hyperparameter search, baselines, external validation, interpretability/explainability (Grad-CAM, SHAP, attention), uncertainty quantification (MC dropout, ensembles, conformal prediction), and robustness/OOD testing — with patient-level partition hygiene throughout. Use when the user plans or reviews a CNN/Transformer/3D/segmentation/detection/foundation/multimodal imaging model, mentions transfer learning, self-supervised, nnU-Net, ViT, data augmentation, class imbalance, explainability, uncertainty, robustness, or "影像深度学习/深度学习模型". Produces a…

Grade

D

Quality

68 / 100

Safety

Passed

GitHub stars

819

Unproven: rated by static analysis only, not yet compared head-to-head.

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