Fellowship Courses After MBBS | Post MBBS Courses | Doctyn https://validator.w3.org/feed/docs/rss2.html Integrated MRCEM Residency Program 9.2.2 Emergency communication protocols 9.2.1 UK-style ED triage system 9.2 Global Readiness 9.1.5 NHS documentation standards 9.1.4 Access to UpToDate / PubMed 9.1.3 Emergency algorithms library 9.1.2 OSCE video bank 9.1.1 2-year Pretest access 9.1 Digital 8.2.2 Skill validation checklists 8.2.1 Procedure logs 8.2 Case logs: 8.1.4 20 airway management assists 8.1.3 20 trauma cases 8.1.2 30 resuscitations 8.1.1 100 acute ED cases 8.1 Minimum case requirements: Clinical Critical Care Mentorship Program Integrated MRCP Residency Program Imaging modalities: X-ray, CT, MRI, Ultrasound, PET (Copy) Image formation principles (conceptual) (Copy) Resolution, contrast, noise & artifacts (Copy) Clinical use cases: 1) TB detection (X-ray) 2) Stroke evaluation (CT) 3) Tumor imaging (MRI) (Copy) Radiology workflow: 1) Order → Scan → PACS → Reporting 2)PACS, RIS, HIS overview (Copy) Where AI helps vs where AI is risky (Copy) Medical imaging data formats: DICOM&NIfTI (Copy) DICOM metadata, tags & PHI (Copy) De-identification & patient privacy (Copy) Image preprocessing: 1) Normalization 2)Resampling 3) Windowing (Copy) Annotation standards & inter-observer variability (Copy) Dataset bias, leakage & quality risks (Copy) NumPy tensor operations (Copy) Image handling with OpenCV&SimpleITK (Copy) Multi-dimensional image visualization (Copy) CT/MRI slice handling (Copy) Conceptual GPU awareness (Copy) CNN fundamentals (medical context) (Copy) U-Net architecture (mandatory) (Copy) Image classification vs segmentation (Copy) Transfer learning (Copy) Class imbalance in medical datasets (Copy) Imaging AI pipeline design (Copy) Data → Model → Inference flow (Copy) Home Medical image transforms (Copy) Loss functions (Dice, Focal – conceptual) (Copy) Training & inference workflows (Copy) Evaluation metrics: 1) Dice, IoU 2) Sensitivity, specificity (Copy) Blogs