Systematic Evaluation of 168 SSL–MIL Combinations for Oncotype DX Prediction

Thumnail. Overall Framework

What problem did I solve?

1. Background

Oncotype DX is expensive and not easily accessible, making treatment decisions difficult in many clinical settings.

❓Research Questions

• Which is more important: Feature Extractor or MIL?
• Does SSL consistently improve performance?
• Which architecture generalizes best across institutions?

🎯 Objective

Systematically evaluate SSL and MIL combinations for robust and generalizable WSI-based Oncotype DX prediction.

How did I solve it?

2. Dataset

Cohort	Purpose
Internal	Training/Test
External	External Test
BCRNet	External Test, Cross-domain
TCGA	External Test, Cross-resource

3. Method

Pipeline: Whole Slide Image → Feature Extraction → MIL → Prediction

• Experimental Design

  markdown 12 Feature Extractors × 14 MIL Models = 168 Experiments

• Evaluation strategy

  markdown 3-fold Cross Validation ↓ 3 Random Seeds ↓ 9 Ensemble Models ↓ 4 Independent Test Cohorts

What were the results?

4. Results

• Finding 1
  : Feature Extractor selection had a larger impact than MIL architecture under external validation.
• Finding 2
  : Attention-based MIL improved cross-institution generalization.
• Finding 3
  : SSL did not consistently outperform supervised models across all cohorts.
• Finding 4
  : Low-risk class remained difficult in 3-class prediction.

5. Discussion & Limitation

What was my contribution?

✅ Literature Review
✅ Dataset Curation
✅ WSI Preprocessing
✅ SSL Feature Extraction
✅ MIL Benchmark
✅ Experimental Design
✅ Statistical Analysis
✅ Visualization
✅ Thesis Writing

What did I learn?

Technical Insight

• Model architecture alone does not guarantee generalization.
• Representation learning plays a larger role than expected.

Research Insight

• External validation changes the ranking of models.
• Evaluation protocol is as important as model development.

Future work

• Pathology Foundation Models
• Multimodal Learning
• Better Multiclass Learning
• Clinical Integration

Appendix.

• Attention Visualization

  

• Related works

  

  

• Feature Extractor - Supplementary

  

  

  

• Multiple Instance Learning - Supplementary

  

• Preprocessing: Patch Extraction

  

  

• Metric - AUROC Range, Silhouette Coefficient

  

  

• Information Utilization Gap