Simplify documentation and remove README license

README.md

@@ -117,6 +117,3 @@ flowchart TD
 
 For csPCa prediction, the backbone is frozen and a 3-layer MLP (`512 → 256 → 128 → 1`) replaces the classification head.
 
-## License
-
-Apache-2.0 — see [LICENSE](LICENSE).

docs/configuration.md

@@ -1,21 +1,11 @@
 # Configuration
 
-## Config System
-
-Configuration follows a three-level hierarchy:
-
-1. **CLI defaults** — Argparse defaults in `run_pirads.py`, `run_cspca.py`, etc.
-2. **YAML overrides** — Values from `--config <file>.yaml` override CLI defaults
-3. **SLURM job name** — If `SLURM_JOB_NAME` is set, it overrides `run_name`
+YAML values always override CLI defaults (`args.__dict__.update(config)`). To override a YAML value, edit the YAML file or omit the key so the CLI default is used.
 
 ```bash
-# CLI defaults are overridden by YAML config
 python run_pirads.py --mode train --config config/config_pirads_train.yaml
 ```
 
-!!! note
-    YAML values **always override** CLI defaults for any key present in the YAML file (`args.__dict__.update(config)`). To override a YAML value, edit the YAML file or omit the key from YAML so the CLI default is used.
-
 ## PI-RADS Training Parameters
 
 | Parameter | Default | Description |
@@ -42,7 +32,7 @@ python run_pirads.py --mode train --config config/config_pirads_train.yaml
 | `wandb` | `False` | Enable Weights & Biases logging |
 | `project_name` | `Classification_prostate` | W&B project name |
 | `run_name` | `train_pirads` | Run name for logging |
-| `dry_run` | `False` | Quick test mode |
+| `dry_run` | `False` | Quick test mode (2 epochs, batch_size=2, no W&B) |
 
 ## csPCa Training Parameters
 
@@ -52,22 +42,16 @@ python run_pirads.py --mode train --config config/config_pirads_train.yaml
 | `config` | — | Path to YAML config file |
 | `data_root` | — | Root folder of images |
 | `dataset_json` | — | Path to dataset JSON file |
-| `num_classes` | `4` | PI-RADS classes (for backbone initialization) |
-| `mil_mode` | `att_trans` | MIL algorithm for backbone |
-| `tile_count` | `24` | Number of patches per scan |
-| `tile_size` | `64` | Patch spatial size |
-| `depth` | `3` | Slices per patch |
-| `use_heatmap` | `True` | Enable heatmap-guided patch sampling |
-| `workers` | `2` | DataLoader workers |
 | `checkpoint_pirads` | — | Path to pre-trained PI-RADS model (required for train) |
 | `checkpoint_cspca` | — | Path to csPCa checkpoint (required for test) |
 | `epochs` | `30` | Max training epochs |
 | `batch_size` | `32` | Scans per batch |
 | `optim_lr` | `2e-4` | Learning rate |
 | `num_seeds` | `20` | Number of random seeds for CI |
-| `val_every` | `1` | Validation frequency |
 | `dry_run` | `False` | Quick test mode |
 
+Shared parameters (`num_classes`, `mil_mode`, `tile_count`, `tile_size`, `depth`, `use_heatmap`, `workers`, `val_every`) have the same defaults as PI-RADS.
+
 ## Preprocessing Parameters
 
 | Parameter | Default | Description |
@@ -131,17 +115,3 @@ python run_pirads.py --mode train --config config/config_pirads_train.yaml
     output_dir: /path/to/processed
     project_dir: /path/to/WSAttention-Prostate
     ```
-
-## Dry-Run Mode
-
-The `--dry_run` flag configures a minimal run for quick testing:
-
-- Epochs: 2
-- Batch size: 2
-- Workers: 0
-- Seeds: 2
-- W&B: disabled
-
-```bash
-python run_pirads.py --mode train --config config/config_pirads_train.yaml --dry_run
-```

docs/getting-started.md

@@ -1,49 +1,17 @@
 # Getting Started
 
-## Prerequisites
-
-- Python 3.11+
-- NVIDIA GPU recommended (CUDA-compatible)
-- ~128 GB RAM for training (configurable via batch size)
-
 ## Installation
 
 ```bash
 git clone https://github.com/anirudhbalaraman/WSAttention-Prostate.git
 cd WSAttention-Prostate
 pip install -r requirements.txt
+pytest tests/  # verify installation
 ```
 
-### External Git Dependencies
-
-Two packages are installed directly from GitHub repositories:
-
-| Package | Source | Purpose |
-|---------|--------|---------|
-| `AIAH_utility` | `ai-assisted-healthcare/AIAH_utility` | Healthcare imaging utilities |
-| `grad-cam` | `jacobgil/pytorch-grad-cam` | Gradient-weighted class activation maps |
-
-These are included in `requirements.txt` and install automatically.
-
-## Verify Installation
-
-Run the test suite in dry-run mode:
-
-```bash
-pytest tests/
-```
-
-Tests use `--dry_run` mode internally (2 epochs, batch_size=2, no W&B).
-
 ## Data Format
 
-Input MRI scans should be in **NRRD** or **NIfTI** format with three modalities per patient:
-
-- T2-weighted (T2W)
-- Diffusion-weighted imaging (DWI)
-- Apparent diffusion coefficient (ADC)
-
-### Dataset JSON Structure
+Input MRI scans should be in **NRRD** or **NIfTI** format with three modalities per patient: T2W, DWI, and ADC.
 
 The data pipeline uses MONAI's decathlon-format JSON:
 
@@ -59,46 +27,8 @@ The data pipeline uses MONAI's decathlon-format JSON:
             "label": 0
         }
     ],
-    "test": [
-        ...
-    ]
+    "test": [...]
 }
 ```
 
-The `image` key points to the T2W image, which serves as the reference modality. Labels for PI-RADS are 0-indexed: label `0` = PI-RADS 2, label `3` = PI-RADS 5. For csPCa, labels are binary (0 or 1).
-
-## Project Structure
-
-```
-WSAttention-Prostate/
-├── run_pirads.py              # PI-RADS training/testing entry point
-├── run_cspca.py               # csPCa training/testing entry point
-├── run_inference.py           # Full inference pipeline
-├── preprocess_main.py         # Preprocessing entry point
-├── config/                    # YAML configuration files
-│   ├── config_pirads_train.yaml
-│   ├── config_pirads_test.yaml
-│   ├── config_cspca_train.yaml
-│   ├── config_cspca_test.yaml
-│   └── config_preprocess.yaml
-├── src/
-│   ├── model/
-│   │   ├── MIL.py             # MILModel_3D — core MIL architecture
-│   │   └── csPCa_model.py     # csPCa_Model + SimpleNN head
-│   ├── data/
-│   │   ├── data_loader.py     # MONAI data pipeline
-│   │   └── custom_transforms.py
-│   ├── train/
-│   │   ├── train_pirads.py    # PI-RADS training loop
-│   │   └── train_cspca.py     # csPCa training loop
-│   ├── preprocessing/
-│   │   ├── register_and_crop.py
-│   │   ├── prostate_mask.py
-│   │   ├── histogram_match.py
-│   │   └── generate_heatmap.py
-│   └── utils.py
-├── job_scripts/               # SLURM job templates
-├── tests/
-├── dataset/                   # Reference images for histogram matching
-└── models/                    # Pre-trained model checkpoints
-```
+Paths are relative to `data_root`. PI-RADS labels are 0-indexed (`0` = PI-RADS 2, `3` = PI-RADS 5). csPCa labels are binary (0 or 1).

docs/index.md

@@ -32,4 +32,3 @@ flowchart LR
 - [Pipeline](pipeline.md) — Full walkthrough of preprocessing, training, and evaluation
 - [Architecture](architecture.md) — Model design and tensor shapes
 - [Configuration](configuration.md) — YAML config reference
-- [Inference](inference.md) — Running predictions on new data

mkdocs.yml

@@ -59,9 +59,7 @@ nav:
   - Pipeline: pipeline.md
   - Architecture: architecture.md
   - Configuration: configuration.md
-  - Inference: inference.md
   - API Reference:
       - Models: api/models.md
       - Preprocessing: api/preprocessing.md
       - Data Loading: api/data.md
-  - Contributing: contributing.md