Tag: Part 2

Part 3: Prepare The Step Where “Model Bugs” Are Usually Born Once scans are organized and annotations exist, the temptation is to treat “prepare” as

Part 2: Organize Convert Every Dataset Into One Uniform “Source of Truth” The “organize” step is where you take raw datasets from hospitals, scanners, and

Part 1: Introduction From DICOM Chaos to Training-Ready Data: Why the Dataset Pipeline Is the Real Model If you’re building algorithms in medical AI, you’ve

Itai Weiss In Part 1, we introduced a 3-layer testing framework for AI projects: unit tests, smoke tests, and golden set tests. Now let’s look