Add/cxr comprehensive tutorial and benchmarks for PyHealth paper

docs/api/interpret.rst

@@ -50,6 +50,13 @@ New to interpretability in PyHealth? Check out these complete examples:
   - Compare different baseline strategies for background sample generation
   - Decode attributions to human-readable medical codes and lab measurements
 
+**ViT/Chefer Attribution Example:**
+
+- ``examples/covid19_cxr_tutorial.py`` - Demonstrates Chefer's attention-based attribution for Vision Transformers:
+
+  - Train a ViT model on COVID-19 chest X-ray classification
+  - Use CheferRelevance for gradient-weighted attention attribution
+  - Visualize which image patches contribute to predictions
 **LIME Example:**
 
 - ``examples/lime_stagenet_mimic4.py`` - Demonstrates LIME (Local Interpretable Model-agnostic Explanations) for StageNet mortality prediction. Shows how to:
@@ -62,8 +69,8 @@ New to interpretability in PyHealth? Check out these complete examples:
 
 These examples provide end-to-end workflows from loading data to interpreting and evaluating attributions.
 
-Available Methods
------------------
+Attribution Methods
+-------------------
 
 .. toctree::
     :maxdepth: 4
@@ -75,4 +82,16 @@ Available Methods
     interpret/pyhealth.interpret.methods.integrated_gradients
     interpret/pyhealth.interpret.methods.shap
     interpret/pyhealth.interpret.methods.lime
-
+
+Visualization Utilities
+-----------------------
+
+The ``pyhealth.interpret.utils`` module provides visualization functions for 
+creating attribution overlays, heatmaps, and publication-ready figures.
+Includes specialized support for Vision Transformer (ViT) attribution visualization.
+
+.. toctree::
+    :maxdepth: 4
+
+    interpret/pyhealth.interpret.utils
+

docs/api/interpret/pyhealth.interpret.utils.rst

@@ -0,0 +1,100 @@
+pyhealth.interpret.utils
+========================
+
+.. automodule:: pyhealth.interpret.utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Overview
+--------
+
+The ``pyhealth.interpret.utils`` module provides visualization utilities for 
+interpretability methods in PyHealth. These functions help create visual
+explanations of model predictions, particularly useful for medical imaging.
+
+Core Functions
+--------------
+
+**Overlay Visualization**
+
+- :func:`show_cam_on_image` - Overlay a CAM/attribution map on an image
+- :func:`visualize_attribution_on_image` - Generate complete attribution visualization
+
+**Normalization & Processing**
+
+- :func:`normalize_attribution` - Normalize attribution values for visualization
+- :func:`interpolate_attribution_map` - Resize attribution to match image dimensions
+
+**Figure Generation**
+
+- :func:`create_attribution_figure` - Create publication-ready figure with overlays
+
+ViT-Specific Functions
+----------------------
+
+These functions are specifically designed for Vision Transformer (ViT) models
+using attention-based interpretability methods like :class:`~pyhealth.interpret.methods.CheferRelevance`.
+
+- :func:`generate_vit_visualization` - Generate visualization components for ViT attribution
+- :func:`create_vit_attribution_figure` - Create complete ViT attribution figure
+- :func:`reshape_vit_attribution` - Reshape flat patch attribution to 2D spatial map
+
+Example: Basic Attribution Visualization
+----------------------------------------
+
+.. code-block:: python
+
+    import numpy as np
+    from pyhealth.interpret.utils import show_cam_on_image, normalize_attribution
+
+    # Assume we have image and attribution from an interpreter
+    image = np.random.rand(224, 224, 3)  # RGB image in [0, 1]
+    attribution = np.random.rand(224, 224)  # Raw attribution values
+
+    # Normalize and overlay
+    attr_normalized = normalize_attribution(attribution)
+    overlay = show_cam_on_image(image, attr_normalized)
+
+Example: ViT Attribution with CheferRelevance
+---------------------------------------------
+
+.. code-block:: python
+
+    from pyhealth.models import TorchvisionModel
+    from pyhealth.interpret.methods import CheferRelevance
+    from pyhealth.interpret.utils import (
+        generate_vit_visualization,
+        create_vit_attribution_figure,
+    )
+    import matplotlib.pyplot as plt
+
+    # Initialize ViT model and interpreter
+    model = TorchvisionModel(dataset, "vit_b_16", {"weights": "DEFAULT"})
+    # ... train model ...
+
+    interpreter = CheferRelevance(model)
+
+    # Generate visualization components
+    image, attr_map, overlay = generate_vit_visualization(
+        interpreter=interpreter,
+        **test_batch
+    )
+
+    # Or create a complete figure
+    fig = create_vit_attribution_figure(
+        interpreter=interpreter,
+        class_names={0: "Normal", 1: "COVID", 2: "Pneumonia"},
+        save_path="vit_attribution.png",
+        **test_batch
+    )
+
+See Also
+--------
+
+- :mod:`pyhealth.interpret.methods` - Attribution methods (DeepLift, IntegratedGradients, CheferRelevance, etc.)
+- :class:`pyhealth.interpret.methods.CheferRelevance` - Attention-based interpretability for Transformers
+- :class:`pyhealth.models.TorchvisionModel` - ViT and other vision models
+
+
+

docs/tutorials.rst

@@ -62,42 +62,50 @@ The ``examples/`` directory contains additional code examples demonstrating vari
 Mortality Prediction
 --------------------
 
+These examples are located in ``examples/mortality_prediction/``.
+
 .. list-table::
    :widths: 50 50
    :header-rows: 1
 
    * - Example File
      - Description
-   * - ``mortality_mimic3_rnn.py``
+   * - ``mortality_prediction/mortality_mimic3_rnn.py``
      - RNN for mortality prediction on MIMIC-III
-   * - ``mortality_mimic3_stagenet.py``
+   * - ``mortality_prediction/mortality_mimic3_stagenet.py``
      - StageNet for mortality prediction on MIMIC-III
-   * - ``mortality_mimic3_adacare.py``
-     - AdaCare for mortality prediction on MIMIC-III
-   * - ``mortality_mimic3_agent.py``
+   * - ``mortality_prediction/mortality_mimic3_adacare.ipynb``
+     - AdaCare for mortality prediction on MIMIC-III (notebook)
+   * - ``mortality_prediction/mortality_mimic3_agent.py``
      - Agent model for mortality prediction on MIMIC-III
-   * - ``mortality_mimic3_concare.py``
+   * - ``mortality_prediction/mortality_mimic3_concare.py``
      - ConCare for mortality prediction on MIMIC-III
-   * - ``mortality_mimic3_grasp.py``
+   * - ``mortality_prediction/mortality_mimic3_grasp.py``
      - GRASP for mortality prediction on MIMIC-III
-   * - ``mortality_mimic3_tcn.py``
+   * - ``mortality_prediction/mortality_mimic3_tcn.py``
      - Temporal Convolutional Network for mortality prediction
-   * - ``mortality_mimic4_stagenet_v2.py``
+   * - ``mortality_prediction/mortality_mimic4_stagenet_v2.py``
      - StageNet for mortality prediction on MIMIC-IV (v2)
+   * - ``mortality_prediction/timeseries_mimic4.py``
+     - Time series analysis on MIMIC-IV
 
 Readmission Prediction
 ----------------------
 
+These examples are located in ``examples/readmission/``.
+
 .. list-table::
    :widths: 50 50
    :header-rows: 1
 
    * - Example File
      - Description
-   * - ``readmission_mimic3_rnn.py``
+   * - ``readmission/readmission_mimic3_rnn.py``
      - RNN for readmission prediction on MIMIC-III
-   * - ``readmission_mimic3_fairness.py``
+   * - ``readmission/readmission_mimic3_fairness.py``
      - Fairness-aware readmission prediction on MIMIC-III
+    * - ``readmission/readmission_omop_rnn.py``
+     - RNN for readmission prediction on OMOP dataset
 
 Survival Prediction
 -------------------
@@ -114,27 +122,29 @@ Survival Prediction
 Drug Recommendation
 -------------------
 
+These examples are located in ``examples/drug_recommendation/``.
+
 .. list-table::
    :widths: 50 50
    :header-rows: 1
 
    * - Example File
      - Description
-   * - ``drug_recommendation_mimic3_safedrug.py``
+   * - ``drug_recommendation/drug_recommendation_mimic3_safedrug.py``
      - SafeDrug for drug recommendation on MIMIC-III
-   * - ``drug_recommendation_mimic3_molerec.py``
+   * - ``drug_recommendation/drug_recommendation_mimic3_molerec.py``
      - MoleRec for drug recommendation on MIMIC-III
-   * - ``drug_recommendation_mimic3_gamenet.py``
+   * - ``drug_recommendation/drug_recommendation_mimic3_gamenet.py``
      - GAMENet for drug recommendation on MIMIC-III
-   * - ``drug_recommendation_mimic3_transformer.py``
+   * - ``drug_recommendation/drug_recommendation_mimic3_transformer.py``
      - Transformer for drug recommendation on MIMIC-III
-   * - ``drug_recommendation_mimic3_micron.py``
+   * - ``drug_recommendation/drug_recommendation_mimic3_micron.py``
      - MICRON for drug recommendation on MIMIC-III
-   * - ``drug_recommendation_mimic4_gamenet.py``
+   * - ``drug_recommendation/drug_recommendation_mimic4_gamenet.py``
      - GAMENet for drug recommendation on MIMIC-IV
-   * - ``drug_recommendation_mimic4_retain.py``
+   * - ``drug_recommendation/drug_recommendation_mimic4_retain.py``
      - RETAIN for drug recommendation on MIMIC-IV
-   * - ``drug_recommendation_eICU_transformer.py``
+   * - ``drug_recommendation/drug_recommendation_eICU_transformer.py``
      - Transformer for drug recommendation on eICU
 
 EEG and Sleep Analysis
@@ -159,27 +169,39 @@ EEG and Sleep Analysis
    * - ``cardiology_detection_isAR_SparcNet.py``
      - SparcNet for cardiology arrhythmia detection
 
-Image Analysis
---------------
+Image Analysis (Chest X-Ray)
+----------------------------
+
+These examples are located in ``examples/cxr/``.
 
 .. list-table::
    :widths: 50 50
    :header-rows: 1
 
    * - Example File
      - Description
-   * - ``covid19cxr_conformal.py``
+   * - ``cxr/covid19cxr_tutorial.py``
+     - ViT training, conformal prediction & interpretability for COVID-19 CXR
+   * - ``cxr/covid19cxr_conformal.py``
      - Conformal prediction for COVID-19 CXR classification
-   * - ``cnn_cxr.ipynb``
+   * - ``cxr/cnn_cxr.ipynb``
      - CNN for chest X-ray classification (notebook)
-   * - ``chestXray_image_generation_VAE.py``
+   * - ``cxr/chestxray14_binary_classification.ipynb``
+     - Binary classification on ChestX-ray14 dataset (notebook)
+   * - ``cxr/chestxray14_multilabel_classification.ipynb``
+     - Multi-label classification on ChestX-ray14 dataset (notebook)
+   * - ``cxr/ChestXrayClassificationWithSaliency.ipynb``
+     - Chest X-ray classification with saliency maps (notebook)
+   * - ``cxr/chextXray_image_generation_VAE.py``
      - VAE for chest X-ray image generation
-   * - ``ChestXray-image-generation-GAN.ipynb``
+   * - ``cxr/ChestXray-image-generation-GAN.ipynb``
      - GAN for chest X-ray image generation (notebook)
 
 Interpretability
 ----------------
 
+These examples are located in ``examples/interpretability/``.
+
 .. list-table::
    :widths: 50 50
    :header-rows: 1
@@ -188,12 +210,20 @@ Interpretability
      - Description
    * - ``integrated_gradients_mortality_mimic4_stagenet.py``
      - Integrated Gradients for StageNet interpretability
-   * - ``deeplift_stagenet_mimic4.py``
+   * - ``interpretability/deeplift_stagenet_mimic4.py``
      - DeepLift attributions for StageNet on MIMIC-IV
-   * - ``interpretability_metrics.py``
+   * - ``interpretability/gim_stagenet_mimic4.py``
+     - GIM attributions for StageNet on MIMIC-IV
+   * - ``interpretability/gim_transformer_mimic4.py``
+     - GIM attributions for Transformer on MIMIC-IV
+   * - ``interpretability/shap_stagenet_mimic4.py``
+     - SHAP attributions for StageNet on MIMIC-IV
+   * - ``interpretability/interpretability_metrics.py``
      - Evaluating attribution methods with metrics
-   * - ``interpret_demo.ipynb``
+   * - ``interpretability/interpret_demo.ipynb``
      - Interactive interpretability demonstrations (notebook)
+   * - ``interpretability/shap_stagenet_mimic4.ipynb``
+     - SHAP attributions for StageNet (notebook)
 
 Patient Linkage
 ---------------
@@ -207,6 +237,22 @@ Patient Linkage
    * - ``patient_linkage_mimic3_medlink.py``
      - MedLink for patient record linkage on MIMIC-III
 
+Length of Stay
+--------------
+
+These examples are located in ``examples/length_of_stay/``.
+
+.. list-table::
+   :widths: 50 50
+   :header-rows: 1
+
+   * - Example File
+     - Description
+   * - ``length_of_stay/length_of_stay_mimic3_rnn.py``
+     - RNN for length of stay prediction on MIMIC-III
+   * - ``length_of_stay/length_of_stay_mimic4_rnn.py``
+     - RNN for length of stay prediction on MIMIC-IV
+
 Advanced Topics
 ---------------
 
@@ -216,13 +262,11 @@ Advanced Topics
 
    * - Example File
      - Description
-   * - ``length_of_stay_mimic3_rnn.py``
-     - RNN for length of stay prediction
    * - ``omop_dataset_demo.py``
      - Working with OMOP Common Data Model
    * - ``medcode.py``
      - Medical code vocabulary and mappings
-   * - ``benchmark_ehrshot.ipynb``
+   * - ``benchmark_ehrshot_xgboost.ipynb``
      - EHRShot benchmark with XGBoost (notebook)
 
 Notebooks (Interactive)
@@ -238,7 +282,7 @@ Notebooks (Interactive)
      - Comprehensive StageNet tutorial
    * - ``mimic3_mortality_prediction_cached.ipynb``
      - Cached mortality prediction workflow
-   * - ``timeseries_mimic4.ipynb``
+   * - ``mortality_prediction/timeseries_mimic4.ipynb``
      - Time series analysis on MIMIC-IV
    * - ``transformer_mimic4.ipynb``
      - Transformer models on MIMIC-IV
@@ -252,7 +296,7 @@ Notebooks (Interactive)
      - SafeDrug interactive notebook
    * - ``molerec_mimic3.ipynb``
      - MoleRec interactive notebook
-   * - ``drug_recommendation_mimic3_micron.ipynb``
+   * - ``drug_recommendation/drug_recommendation_mimic3_micron.ipynb``
      - MICRON interactive notebook
    * - ``kg_embedding.ipynb``
      - Knowledge graph embeddings