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Anirudh Balaraman commited on
Fix HTML entities and enhance pipeline documentation
Browse filesUpdated the pipeline documentation to improve clarity and fix HTML entities.
- docs/pipeline.md +5 -3
docs/pipeline.md
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@@ -4,18 +4,18 @@ The full pipeline has three phases: preprocessing, PI-RADS training (Stage 1), a
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```mermaid
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flowchart TD
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subgraph
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R[register_and_crop] --> S[get_segmentation_mask]
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S --> H[histogram_match]
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H --> G[get_heatmap]
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end
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subgraph Stage 1
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P[
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end
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subgraph Stage 2
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C[
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end
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G --> P
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@@ -96,3 +96,5 @@ python run_cspca.py --mode train --config config/config_cspca_train.yaml
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| Metrics | AUC, Sensitivity, Specificity |
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The backbone's feature extractor (`net`), transformer, and `myfc` are frozen. The attention module and `SimpleNN` classification head are trained. After training the framework reports mean and 95% confidence intervals for AUC, sensitivity, and specificity by testing across 20 random seeds.
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```mermaid
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flowchart TD
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subgraph Preprocessing
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R[register_and_crop] --> S[get_segmentation_mask]
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S --> H[histogram_match]
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H --> G[get_heatmap]
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end
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subgraph Stage 1
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P[PI-RADS Training<br/>CrossEntropy + Attention Loss]
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end
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subgraph Stage 2
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C[csPCa Training<br/>Frozen Backbone + BCE Loss]
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end
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G --> P
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| Metrics | AUC, Sensitivity, Specificity |
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The backbone's feature extractor (`net`), transformer, and `myfc` are frozen. The attention module and `SimpleNN` classification head are trained. After training the framework reports mean and 95% confidence intervals for AUC, sensitivity, and specificity by testing across 20 random seeds.
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Refer to [Getting Started](getting-started.md) for JSON dataset format to run run_pirads.py and run_cspca.py
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