e-learning
Gleam Multimodal Learner - Head and Neck cancer Recurrence Prediction with HANCOCK
Abstract
In this tutorial, we use the HANCOCK head-and-neck cancer cohort to build a recurrence prediction model with GLEAM Multimodal Learner.
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- How does Multimodal Learner combine tabular, text, and image modalities in a single model?
- How do we configure Multimodal Learner with different modalities?
- How do we interpret the model results?
Learning Objectives
- Import the tutorial-focused HANCOCK metadata and JPEG CD3/CD8 image archive into Galaxy.
- Train a multimodal model with tabular, text, and image backbones.
- Evaluate test performance and interpret the effect of image quality on model metrics.
Licence: Creative Commons Attribution 4.0 International
Keywords: GLEAM, HANCOCK Dataset, Multimodal Learning, Recurrence Prediction, Statistics and machine learning
Target audience: Students
Resource type: e-learning
Version: 1
Status: Active
Learning objectives:
- Import the tutorial-focused HANCOCK metadata and JPEG CD3/CD8 image archive into Galaxy.
- Train a multimodal model with tabular, text, and image backbones.
- Evaluate test performance and interpret the effect of image quality on model metrics.
Date modified: 2026-03-25
Date published: 2026-03-25
Contributors: Anup Kumar, Paulo Cilas Morais Lyra Junior, Saskia Hiltemann
Scientific topics: Statistics and probability
External resources:
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