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High-Resolution Heatmaps for Marugoto MIL Models

Options

create_heatmaps.py [-h] -m MODEL_PATH -o OUTPUT_PATH -t TRUE_CLASS
                   [--no-pool]
                   [--mask-threshold THRESH]
                   [--att-upper-threshold THRESH]
                   [--att-lower-threshold THRESH]
                   [--score-threshold THRESH]
                   [--att-cmap CMAP]
                   [--score-cmap CMAP]
                   SLIDE [SLIDE ...]

Create heatmaps for MIL models.

Positional Arguments Description
SLIDE Slides to create heatmaps for. If multiple slides are given, the normalization of the attention / score maps' intensities will be performed across all slides.
Options Description
-m MODEL_PATH, --model-path MODEL_PATH MIL model used to generate attention / score maps.
-o OUTPUT_PATH, --output-path OUTPUT_PATH Path to save results to.
-t TRUE_CLASS, --true-class TRUE_CLASS Class to be rendered as "hot" in the heatmap.
--no-pool Do not average pool features after feature extraction phase.
--cache-dir CACHE_DIR Directory to cache extracted features etc. in.
Thresholds Description
--mask-threshold THRESH Brightness threshold for background removal.
--att-upper-threshold THRESH Quantile to squash attention from during attention scaling (e.g. 0.99 will lead to the top 1% of attention scores to become 1)
--att-lower-threshold THRESH Quantile to squash attention to during attention scaling (e.g. 0.01 will lead to the bottom 1% of attention scores to become 0)
--score-threshold THRESH Quantile to consider in score scaling (e.g. 0.95 will discard the top / bottom 5% of score values as outliers)
Colors Description
--att-cmap CMAP Color map to use for the attention heatmap.
--score-cmap CMAP Color map to use for the score heatmap.

Running in a Container

The heatmap script can be conveniently run in a podman container. To do so, use the heatmaps-container.sh convenience script.

./heatmaps-container.sh \
    -t TRUE_CLASS \
    /wsis/slide1.svs \
    /wsis/slide2.svs

In order to use GPU acceleration, the nvidia-container-toolkit has to be installed beforehand.