slides
Gleam Image Learner - Validating Skin Lesion Classification on HAM10000
Abstract
Introduction to GLEAM Image Learner and Galaxy
Questions this will address
- How do we validate GLEAM's Image Learner against a published benchmark on HAM10000?
- How do we set up a balanced train/validation/test split for multi-class image classification?
- How do we interpret accuracy, weighted precision/recall, and weighted F1 for imbalanced medical imaging datasets?
Learning Objectives
- Prepare a balanced HAM10000 subset and perform a stratified 70/10/20 train/validation/test split.
- Train an Image Learner model using a pretrained CaFormer S18 384 backbone.
- Evaluate performance using accuracy and weighted precision/recall/F1, and inspect confusion patterns.
Licence: Creative Commons Attribution 4.0 International
Keywords: Statistics and machine learning
Target audience: Students
Resource type: slides
Version: 1
Status: Active
Learning objectives:
- Prepare a balanced HAM10000 subset and perform a stratified 70/10/20 train/validation/test split.
- Train an Image Learner model using a pretrained CaFormer S18 384 backbone.
- Evaluate performance using accuracy and weighted precision/recall/F1, and inspect confusion patterns.
Date modified: 2026-01-28
Date published: 2026-01-28
Contributors: Anup Kumar, Paulo Cilas Morais Lyra Junior, Saskia Hiltemann
Scientific topics: Statistics and probability
External resources:
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