Explainability

"Which architecture for which research question" decision tool: maps task, modality, data scale, and class imbalance to a paper-grounded architecture shortlist — each with the source paper, when-to-use, medical-imaging use, reference implementation, and the matching scaffold template.

Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, synthesis, or self-supervised pretraining — with a patient-level seed-locked split, train/evaluate scripts, and a Methods stub. Integrates MONAI / nnU-Net, never reimplements them.

Design or audit the clinical-validation study for an engineer-built medical-imaging model: patient-level split disjointness, the data-leakage taxonomy, internal vs external validation, comparator design, and task-correct metric selection — with a deterministic split-leakage gate.

Generate the documentation an engineer-built model must carry — a Model Card (Mitchell et al. 2019), a Datasheet for its dataset (Gebru et al. 2021), and a METRIC data-quality pass — filled only from user-supplied facts, then verify every required section is present with a completeness gate.