feat: tile masks

configs/dataset/tiled/ftn/0_320.yaml

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+defaults:
+  - /dataset/processed_w_masks/ftn@_here_
+  - _self_
+
+mpp: 0.17
+tile_extent: 320
+level: 0
+tiling_uris:
+  train: "mlflow-artifacts:/86/bbbe4603bc30495d85ac99093fc9269a/artifacts/train - ftn" # TODO update URI
+  test_preliminary: "mlflow-artifacts:/86/bbbe4603bc30495d85ac99093fc9269a/artifacts/test preliminary - ftn" # TODO update URI
+  test_final: "mlflow-artifacts:/86/bbbe4603bc30495d85ac99093fc9269a/artifacts/test final - ftn" # TODO update URI

configs/dataset/tiled/ftn/0_430.yaml

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+defaults:
+  - /dataset/processed_w_masks/ftn@_here_
+  - _self_
+
+mpp: 0.17
+tile_extent: 430
+level: 0
+tiling_uris:
+  train: "mlflow-artifacts:/86/de450f835f0d4462a91b35f4a79a500f/artifacts/train - ftn" # TODO update URI
+  test_preliminary: "mlflow-artifacts:/86/de450f835f0d4462a91b35f4a79a500f/artifacts/test preliminary - ftn" # TODO update URI
+  test_final: "mlflow-artifacts:/86/de450f835f0d4462a91b35f4a79a500f/artifacts/test final - ftn" # TODO update URI

configs/dataset/tiled/ftn/1_224.yaml

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+defaults:
+  - /dataset/processed_w_masks/ftn@_here_
+  - _self_
+
+mpp: 0.52
+tile_extent: 224
+level: 1
+tiling_uris:
+  train: "mlflow-artifacts:/86/f85b64a7f96c41e38f86d84956e2dbe9/artifacts/train - ftn" # TODO update URI
+  test_preliminary: "mlflow-artifacts:/86/f85b64a7f96c41e38f86d84956e2dbe9/artifacts/test preliminary - ftn" # TODO update URI
+  test_final: "mlflow-artifacts:/86/f85b64a7f96c41e38f86d84956e2dbe9/artifacts/test final - ftn" # TODO update URI

configs/dataset/tiled/ftn/2_224.yaml

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+defaults:
+  - /dataset/processed_w_masks/ftn@_here_
+  - _self_
+
+mpp: 1.55
+tile_extent: 224
+level: 2
+tiling_uris:
+  train: "mlflow-artifacts:/86/5814484b6cd7467e9d712889655479af/artifacts/train - ftn" # TODO update URI
+  test_preliminary: "mlflow-artifacts:/86/5814484b6cd7467e9d712889655479af/artifacts/test preliminary - ftn" # TODO update URI
+  test_final: "mlflow-artifacts:/86/5814484b6cd7467e9d712889655479af/artifacts/test final - ftn" # TODO update URI

configs/dataset/tiled/ikem/0_320.yaml

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+defaults:
+  - /dataset/processed_w_masks/ikem@_here_
+  - _self_
+
+mpp: 0.17
+tile_extent: 320
+level: 0
+tiling_uris:
+  train: "mlflow-artifacts:/86/4486e598446d412d926ac66dadb35e51/artifacts/train - ikem" # TODO update URI
+  test_preliminary: "mlflow-artifacts:/86/4486e598446d412d926ac66dadb35e51/artifacts/test preliminary - ikem" # TODO update URI
+  test_final: "mlflow-artifacts:/86/4486e598446d412d926ac66dadb35e51/artifacts/test final - ikem" # TODO update URI

configs/dataset/tiled/ikem/0_430.yaml

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+defaults:
+  - /dataset/processed_w_masks/ikem@_here_
+  - _self_
+
+mpp: 0.17
+tile_extent: 430
+level: 0
+tiling_uris:
+  train: "mlflow-artifacts:/86/fd112e63819c49d999502542b35bfce1/artifacts/train - ikem" # TODO update URI
+  test_preliminary: "mlflow-artifacts:/86/fd112e63819c49d999502542b35bfce1/artifacts/test preliminary - ikem" # TODO update URI
+  test_final: "mlflow-artifacts:/86/fd112e63819c49d999502542b35bfce1/artifacts/test final - ikem" # TODO update URI

configs/dataset/tiled/ikem/1_224.yaml

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+defaults:
+  - /dataset/processed_w_masks/ikem@_here_
+  - _self_
+
+mpp: 0.52
+tile_extent: 224
+level: 1
+tiling_uris:
+  train: "mlflow-artifacts:/86/ece822e7c0e3416f97212267c773c8ac/artifacts/train - ikem" # TODO update URI
+  test_preliminary: "mlflow-artifacts:/86/ece822e7c0e3416f97212267c773c8ac/artifacts/test preliminary - ikem" # TODO update URI
+  test_final: "mlflow-artifacts:/86/ece822e7c0e3416f97212267c773c8ac/artifacts/test final - ikem" # TODO update URI

configs/dataset/tiled/ikem/2_224.yaml

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+defaults:
+  - /dataset/processed_w_masks/ikem@_here_
+  - _self_
+
+mpp: 1.55
+tile_extent: 224
+level: 2
+tiling_uris:
+  train: "mlflow-artifacts:/86/0c09e1c61d294fa3877b6b21703bab2f/artifacts/train - ikem" # TODO update URI
+  test_preliminary: "mlflow-artifacts:/86/0c09e1c61d294fa3877b6b21703bab2f/artifacts/test preliminary - ikem" # TODO update URI
+  test_final: "mlflow-artifacts:/86/0c09e1c61d294fa3877b6b21703bab2f/artifacts/test final - ikem" # TODO update URI

configs/dataset/tiled/knl_patos/0_320.yaml

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+defaults:
+  - /dataset/processed_w_masks/knl_patos@_here_
+  - _self_
+
+mpp: 0.17
+tile_extent: 320
+level: 0
+tiling_uris:
+  test_preliminary: "mlflow-artifacts:/86/7b9a446145b14965981bbac88e8e2c8b/artifacts/test preliminary - knl_patos" # TODO update URI
+  test_final: "mlflow-artifacts:/86/7b9a446145b14965981bbac88e8e2c8b/artifacts/test final - knl_patos" # TODO update URI

configs/dataset/tiled/knl_patos/0_430.yaml

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+defaults:
+  - /dataset/processed_w_masks/knl_patos@_here_
+  - _self_
+
+mpp: 0.17
+tile_extent: 430
+level: 0
+tiling_uris:
+  test_preliminary: "mlflow-artifacts:/86/eb29255c944d4dad926160a7cb102ad9/artifacts/test preliminary - knl_patos" # TODO update URI
+  test_final: "mlflow-artifacts:/86/eb29255c944d4dad926160a7cb102ad9/artifacts/test final - knl_patos" # TODO update URI

configs/dataset/tiled/knl_patos/1_224.yaml

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+defaults:
+  - /dataset/processed_w_masks/knl_patos@_here_
+  - _self_
+
+mpp: 0.52
+tile_extent: 224
+level: 1
+tiling_uris:
+  test_preliminary: "mlflow-artifacts:/86/6782155362d54ecc9f1beccb4362d359/artifacts/test preliminary - knl_patos" # TODO update URI
+  test_final: "mlflow-artifacts:/86/6782155362d54ecc9f1beccb4362d359/artifacts/test final - knl_patos" # TODO update URI

configs/dataset/tiled/knl_patos/2_224.yaml

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+defaults:
+  - /dataset/processed_w_masks/knl_patos@_here_
+  - _self_
+
+mpp: 1.55
+tile_extent: 224
+level: 2
+tiling_uris:
+  test_preliminary: "mlflow-artifacts:/86/d7486bb6b667433989c3ce1c8ce31d60/artifacts/test preliminary - knl_patos" # TODO update URI
+  test_final: "mlflow-artifacts:/86/d7486bb6b667433989c3ce1c8ce31d60/artifacts/test final - knl_patos" # TODO update URI

configs/preprocessing/tile_masks.yaml

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+# @package _global_
+
+max_concurrent: 64
+artifact_path: tile_masks
+level: 3
+
+metadata:
+  run_name: "🎭 Tile Masks: ${dataset.institution}"
+  description: Tile masks for ${dataset.institution} at tiling level ${dataset.level}
+  hyperparams:
+    mask_level: ${level}
+    tiling_level: ${dataset.level}

preprocessing/tile_masks.py

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+from collections.abc import Iterable
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import cast
+
+import hydra
+import mlflow.artifacts
+import numpy as np
+import pandas as pd
+import pyvips
+import ray
+from omegaconf import DictConfig
+from openslide import OpenSlide
+from rationai.masks import process_items, slide_resolution, tile_mask, write_big_tiff
+from rationai.mlkit import autolog, with_cli_args
+from rationai.mlkit.lightning.loggers import MLFlowLogger
+
+
+def download_slide_tiles(uris: Iterable[str]) -> tuple[pd.DataFrame, pd.DataFrame]:
+    slidess, tiless = [], []
+    for uri in uris:
+        path = mlflow.artifacts.download_artifacts(artifact_uri=uri)
+        slidess.append(pd.read_parquet(Path(path) / "slides.parquet"))
+        tiless.append(pd.read_parquet(Path(path) / "tiles.parquet"))
+
+    slides = pd.concat(slidess).reset_index(drop=True)
+    tiles = pd.concat(tiless).reset_index(drop=True)
+    return slides, tiles
+
+
+@ray.remote(memory=4 * 1024**3)
+def process_slide(
+    slide: pd.Series,
+    tiles: pd.DataFrame,  # tiles are automatically serialized by Ray
+    level: int,
+    output_folder: Path,
+) -> None:
+    with OpenSlide(slide["path"]) as slide_wsi:
+        mask_extent_x, mask_extent_y = slide_wsi.level_dimensions[level]
+        mpp_x, mpp_y = slide_resolution(slide_wsi, level)
+
+    slide_tiles = tiles[tiles["slide_id"] == slide.id]
+
+    blur_slide_tiles = slide_tiles[slide_tiles["blur"] > 0.25]
+    artifacts_slide_tiles = slide_tiles[slide_tiles["artifacts"] > 0.25]
+
+    drop_indices = blur_slide_tiles.index.union(artifacts_slide_tiles.index)
+    slide_tiles = slide_tiles.drop(drop_indices)
+
+    for folder, tiles_subset in [
+        ("blur_tiles", blur_slide_tiles),
+        ("artifacts_tiles", artifacts_slide_tiles),
+        ("clean_tiles", slide_tiles),
+    ]:
+        if tiles_subset.empty:
+            continue
+
+        mask = tile_mask(
+            tiles_subset,
+            tile_extent=(slide["tile_extent_x"], slide["tile_extent_y"]),
+            size=(slide["extent_x"], slide["extent_y"]),
+        )
+        mask = cast("pyvips.Image", pyvips.Image.new_from_array(np.array(mask)))
+        mask = cast(
+            "pyvips.Image",
+            mask.resize(mask_extent_x / mask.width, vscale=mask_extent_y / mask.height),  # type: ignore[reportCallIssue]
+        )
+
+        mask_path = output_folder / folder / f"{Path(slide['path']).stem}.tiff"
+        mask_path.parent.mkdir(parents=True, exist_ok=True)
+        write_big_tiff(mask, mask_path, mpp_x=mpp_x, mpp_y=mpp_y)
+
+
+@with_cli_args(["+preprocessing=tile_masks"])
+@hydra.main(config_path="../configs", config_name="preprocessing", version_base=None)
+@autolog
+def main(config: DictConfig, logger: MLFlowLogger) -> None:
+    slides, tiles = download_slide_tiles(config.dataset.tiling_uris.values())
+    tiles_ref = ray.put(tiles)
+
+    with TemporaryDirectory() as output_dir:
+        process_items(
+            (slide for _, slide in slides.iterrows()),
+            process_item=process_slide,
+            fn_kwargs={
+                "tiles": tiles_ref,
+                "level": config.level,
+                "output_folder": Path(output_dir),
+            },
+            max_concurrent=config.max_concurrent,
+        )
+        logger.log_artifacts(local_dir=output_dir, artifact_path=config.artifact_path)
+
+
+if __name__ == "__main__":
+    ray.init(runtime_env={"excludes": [".git", ".venv"]})
+    main()

scripts/preprocessing/tile_masks.py

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+from kube_jobs import storage, submit_job
+
+
+submit_job(
+    job_name="ulcerative-colitis-tile-masks-...",
+    username=...,
+    public=False,
+    cpu=64,
+    memory="32Gi",
+    script=[
+        "git clone https://github.com/RationAI/ulcerative-colitis.git workdir",
+        "cd workdir",
+        "uv sync --frozen",
+        "uv run --active -m preprocessing.tile_masks +dataset=tiled/.../...",
+    ],
+    storage=[storage.secure.Data],
+)