| # 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. |
|
|
| ```bash |
| 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 |
|
|
| ```bash |
| 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 |
|
|
| ```bash |
| 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`. |
|
|
