Engineering for Annotation in the ML Pipeline
Engineering for Annotation in the ML Pipeline, Part 1 Engineering for Annotation in the ML Pipeline, Part 1: Designing a Testable Protocol Author: Eytan Slotnik
Tag: GenAI
From DICOM Chaos to Training-Ready Data: Our Dataset Pipeline for Medical AI – Part 4
Part 4: Validation + Test Making Your Metric Trustworthy (and Your Test Set Worth Something) At some point, your model gets “good enough” that progress
From DICOM Chaos to Training-Ready Data: Our Dataset Pipeline for Medical AI – Part 3
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
From DICOM Chaos to Training-Ready Data: Our Dataset Pipeline for Medical AI – Part 2
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
From DICOM Chaos to Training-Ready Data: Our Dataset Pipeline for Medical AI – Part 1
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
Continuous Integration for AI Projects – Part 2
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
Continuous Integration for AI Projects
Itai Weiss Continuous Integration (CI) is the practice of automatically testing your code every time you make a change. In traditional software, this works well
GenAI in Medical Imaging
Arie Rond and David Menashe Artificial intelligence (AI) is already transforming healthcare, enabling capabilities that seemed unattainable a decade ago. Now, a new frontier, generative
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