Patient-Level Stratified KFold

Overview

In medical imaging, each patient has multiple images (e.g., left/right breast, multiple slices, follow-up scans). Standard image-level KFold leaks information — images from the same patient can appear in both train and validation, inflating metrics by 0.01–0.05. Patient-level stratification ensures all images from one patient are in the same fold, while still balancing the target distribution across folds. Essential for any medical competition (RSNA, SIIM, VinBigData).

Quick Start

import pandas as pd
from sklearn.model_selection import StratifiedKFold

# Aggregate to patient-level label (any positive image = positive patient)
patient_labels = train_df.groupby('patient_id')['target'].max().reset_index()

# Split at patient level, stratified by patient-level label
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
patient_labels['fold'] = -1
for fold, (_, val_idx) in enumerate(skf.split(
    patient_labels['patient_id'], patient_labels['target']
)):
    patient_labels.loc[val_idx, 'fold'] = fold

# Map fold back to image-level DataFrame
train_df = train_df.merge(
    patient_labels[['patient_id', 'fold']], on='patient_id'
)

# Use in training
for fold in range(5):
    train_idx = train_df[train_df['fold'] != fold].index
    val_idx = train_df[train_df['fold'] == fold].index
    # No patient overlap between train_idx and val_idx

Workflow

1. Aggregate target to patient level (groupby('patient_id').target.max())
2. Run StratifiedKFold on patient-level DataFrame
3. Assign fold numbers to patients
4. Merge fold assignments back to image-level DataFrame
5. All images from one patient are in the same fold

Key Decisions

• Aggregation: max() for binary (any positive = positive patient); mean() for regression
• Stratification: On patient-level label, not image-level — ensures balanced class distribution
• GroupKFold alternative: GroupKFold prevents leakage but doesn't stratify; this does both
• Multi-label: For multi-condition labels, stratify on the rarest positive condition

References

• fast.ai Starter Pack - Train + Inference