duyle2408/results_new_collapse_research

MILK10k Collapse Research

Why this folder exists

The main training pipeline (milk10k_effb2_metadata) does full end-to-end dual-encoder training — hours per run, heavy GPU, lots of hyperparameters. That's fine for final experiments, but terrible for iterating on new ideas.

This folder is a lightweight research sandbox. The idea is:

1. Extract frozen features from a foundation model once (DINOv2, ConvNeXt, etc.)
2. Train cheap sklearn probes (minutes, CPU) on those features
3. Test collapse-mitigation ideas fast without re-training the encoder

Results go to results/new_collapse_research/ and never interfere with the controlled dual-encoder runs.

The problem: class collapse

MILK10k has a severe long-tail distribution:

• Head: NV, MEL, BCC, BKL
• Tail: BEN_OTH, INF, MAL_OTH (called "tail gate" classes)

The tail classes get crushed — the model rarely predicts them. This manifests as low recall on BEN_OTH/INF/MAL_OTH even when overall metrics look fine.

The BASELINE_F1_MACRO = 0.5828 is the reference point to beat.

The pipeline

extract_foundation_features.py    # Step 1: freeze a backbone, dump .npz features
          │
          ├── linear_probe.py           # Quick baseline: logistic/MLP on frozen features
          ├── train_hierarchical.py     # Two-stage: group → within-group expert
          ├── train_multimodal_ssl.py   # Contrastive pretrain on pairs, then linear eval
          │
analyze_decision_policy.py        # Post-hoc: optimize tail-class logit biases

Feature extraction (extract_foundation_features.py)

Runs a frozen model (DINOv2, or any timm model) on all clinical/dermoscopic pairs and saves features as .npz. Features are L2-normalized by default.

Output for each split: {train,val}_features.npz containing clinical, dermoscopic, lesion_id, and label arrays.

Linear probe (linear_probe.py)

Trains a LogisticRegression or MLPClassifier on frozen features. Supports three feature modes:

• pair — concatenates [clinical, dermoscopic, |clinical-dermoscopic|, clinical×dermoscopic]
• clinical — clinical image features only
• dermoscopic — dermoscopic image features only

Useful for: establishing a feature-quality baseline, comparing backbones, testing whether the frozen DINOv2 features already contain useful signal.

Hierarchical probe (train_hierarchical.py)

Two-stage classification:

1. Group classifier — predicts one of three groups:
   • melanocytic (MEL, NV)
   • keratinocyte_like (AKIEC, BCC, BKL, SCCKA)
   • rare_other (BEN_OTH, INF, MAL_OTH, DF, VASC)
2. Per-group expert — predicts the fine class within the predicted group

The final probability = P(group) × P(class | group), renormalized.

Useful for: testing whether partitioning the problem reduces tail-class confusion. A rare-class like BEN_OTH only competes within rare_other instead of all 11 classes.

Multimodal SSL (train_multimodal_ssl.py)

Contrastively pretrains an encoder on clinical-dermoscopic pairs (positive pairs are clinical and dermoscopic images of the same lesion) using a symmetric CLIP loss. After pretraining, evaluates via linear probe on frozen encoder features.

Useful for: learning representations that are invariant to the clinical/dermoscopic modality shift, potentially improving downstream classification.

Decision policy (analyze_decision_policy.py)

Post-hoc optimization: searches for logit biases on tail classes (BEN_OTH, INF, MAL_OTH) that maximize macro F1 on the validation set.

Useful for: squeezing the last bit of performance from an already-trained model without re-training. The --tail-only flag restricts optimization to just the three tail gate classes.

How to run

# Activate your environment first
conda activate ml2

# Full pipeline (features → probes → SSL → policy)
DATA_DIR=/path/to/milk10k bash milk10k_new_collapse_research/run_new_ideas.sh

# Only feature extraction with a different backbone
MODEL_NAME=dinov2_vits14 bash milk10k_new_collapse_research/run_new_ideas.sh

# Only decision policy on an existing prediction CSV
PREDICTIONS_CSV=/path/to/val_predictions.csv bash milk10k_new_collapse_research/run_new_ideas.sh

Each step skips if its outputs already exist, so you can rerun selectively.

Key metrics file

Every script writes to results/new_collapse_research/{experiment}/:

• metrics.json — overall metrics (f1_macro, balanced_accuracy, etc.)
• per_class_metrics.csv — per-class recall, precision, f1
• confusion_matrix.csv — full 11×11 confusion matrix
• collapse_report.md — human-readable report focused on tail-gate classes