Shevs Connect Institute | SCILabel | Medical AI Annotation Company & Ethical Data Marketplace https://validator.w3.org/feed/docs/rss2.html Blog PRACTICE ASSIGNMENT 5 LIVE SESSION 5 RECORDING: Advanced medical image annotation — segmentation, quality review and introduction to SCILabel Spotting good vs. poor annotations: a visual field guide to image annotation errors Medical image annotation quality assurance: the six most common rejection categories Polygon annotation technique in CVAT: anchors, curves and precision tips Semantic vs. instance segmentation: definitions, differences and annotation decisions SCI- HEALTHCARE AI TRAINER & DATA ANNOTATION PROGRAM Advanced object segmentation: polygons vs. bounding boxes — when to use each PRACTICE ASSIGNMENT 4 LIVE SESSION 4 RECORDING: Response evaluation & ranking, and biomedical / pharmaceutical AI annotation Home Biological pathway and mechanism annotation: KEGG and Reactome reference guide for annotators Biomarker classification: diagnostic, prognostic and predictive biomarker annotation Biomedical entity recognition: disease-gene associations, gene/protein mentions and HGVS mutation notation Protocol deviation flagging and ICH-GCP data integrity annotation in clinical trial records Clinical benefit-risk language annotation in pharmaceutical regulatory documents Pharmacovigilance safety signal detection: annotating black box warning triggers and on-label vs. off-label use Clinical trial data annotation: eligibility criteria, endpoints, adverse event classification and CTCAE grading Drug-drug interaction and contraindication annotation Drug dose, route, frequency and indication labelling Adverse drug reaction (ADR) annotation: MedDRA preferred terms, LLT-to-PT mapping and WHO-UMC causality grading Drug and compound annotation: generic names, brand names, drug classes and mechanisms of action AI safety fundamentals for healthcare annotators: identifying harmful and unsafe AI outputs PRACTICE ASSIGNMENT 3 LIVE SESSION 3 RECORDING: Image annotation — tools, modalities, bounding-box annotation and object classification Data Collection PRACTICE ASSIGNMENT 2 LIVE SESSION 2 RECORDING:RLHF fundamentals, annotator mindset, source verification, guidelines and corrective feedback writing General Swedish for Professionals A1-2026 Worked examples: good vs. poor corrective feedback, side by side Swedish for Medical Professionals Cohort I-2026 Program Lesson 23 Assignment 2 Assignment 14 B1 Lesson 2 Data & Model Evaluation Corrective feedback writing: the five principles — atomicity, actionability, span-level specificity, tone and comprehensiveness PRACTICE ASSIGNMENT 1 Our Services footer How It Works header Data Annotation & Labeling About Shevs Connect Institute Lesson 24 B1Lesson 3 LIVE SESSION 1 RECORDING: Foundations — the healthcare AI landscape, data quality, clinical text and the annotator’s core toolkit How Specialized AI Data Annotation is Transforming Healthcare, Life Sciences, Insurance, and Clinical Research Sales Representative Registration Form