README.md

MILK10k EfficientNet-B2 dermoscopic metadata

Standalone single-image pipeline. It reads only rows whose image_type is dermoscopic; clinical images and clinical metadata are never loaded. --data-dir contains the metadata and ground-truth CSV files. Images may be under that directory or its parent; otherwise pass --input-dir explicitly.

Matched metadata ablation

Run from this directory. The split manifest is created by the first run and reused verbatim by the second run.

python run_metadata_ablation.py \
  --data-dir ../data_related \
  --output-dir ../results_dermoscopic_metadata_ablation \
  --split-manifest ../results_dermoscopic_metadata_ablation/split.json \
  -- --amp --loss ce_dice --class-weight --freeze-epochs 5 --finetune-epochs 20

The comparison is written to ablation_summary.csv, ablation_summary.json, and ablation_per_class.csv. Calibration is intentionally disabled by the ablation runner so raw validation results are comparable.

One training run

python train_milk10k_effb2_dermoscopic_metadata.py \
  --data-dir ../data_related \
  --output-dir ../dermoscopic_with_metadata \
  --split-manifest ../results_dermoscopic_metadata_ablation/split.json \
  --metadata-mode concat --amp --loss ce_dice

--metadata-mode accepts none, concat, gated_concat, and gated_only. Use --encoder-checkpoint to initialize only the image encoder and --resume-checkpoint RUN/last.pt to continue an interrupted run.

Losses: ce, focal, ldam, ce_dice, and ce_f1. For generated datasets, use --synthetic-train-only so __sdpair_ lesions cannot enter validation. Additional generated data can be appended with --augmented-data-dir, filtered with --augmented-classes, and capped with --augmented-max-per-class.

Use --selection-metric f1_macro (default) or dice_macro. LDAM runs also write tail_best.pt. Pass --k-folds N to create deterministic folds in the shared split manifest.

Inference

python predict_milk10k_effb2_dermoscopic_metadata.py \
  --checkpoint ../dermoscopic_with_metadata/best.pt \
  --input-dir ../MILK10k_Test_Input \
  --metadata-csv /path/to/MILK10k_Test_Metadata.csv \
  --output ../test_dermoscopic_predictions.csv --tta-flips

If a labeled set is supplied with --groundtruth-csv, inference also writes overall, per-class, and confusion-matrix metrics. A sibling calibration.json is loaded automatically unless --no-auto-calibration is passed.

Outputs

Training writes best.pt, last.pt, history.csv, metrics.json, per_class_metrics.csv, confusion_matrix.csv, val_predictions.csv, splits/, run_config.json, data_summary.json, split_summary.md, prediction/confusion diagnostics, and run_report.md. K-fold runs additionally write kfold_summary.csv/json and kfold_report.md.