medscan-cu116 0.1.2
Medscan with CUDA 11.6 (PyTorch GPU support)
Requires Python: >=3.10

# MedScan – End‑to‑End Medical Imaging Training Pipeline

MedScan is a self‑contained Python package that lets you **pre‑process and augment data**, **train deep‑learning models**, **evaluate with plots/metrics**, and **save / reload checkpoints** – all from **one intuitive API** **or** a single **command‑line call**.

---

## Table of Contents

1. [Key Features](#key-features)
2. [Repository Layout](#repository-layout)
3. [Installation](#installation)
4. [Quick‑Start Notebook](#quick-start-notebook)
5. [Command‑Line Usage](#command-line-usage)
6. [Prediction CLI](#prediction-cli)
7. [API Overview](#api-overview)
8. [Training Pipeline Walk‑Through](#training-pipeline-walk-through)
9. [Saving, Loading & Plot Outputs](#saving-loading--plot-outputs)
10. [Troubleshooting](#troubleshooting)
11. [License](#license)

---

## Key Features

* **One‑line data split** with `Data.split()` (stratified, group‑aware, or plain).
* **Config‑driven** augmentation, balancing, masking & context‐feature handling via `PreprocessConfig`.
* **Flexible training**:

  * **Multi‑head** model (shared backbone, one head per target) *or*
  * **Single‑head‑per‑label** models (optionally across multiple backbones).
* **Torch AMP** support (mixed precision) & automatic GPU/CPU selection.
* **Automatic early stopping** per head or per model.
* **Rich evaluation** with confusion matrices, loss & LR curves, AUC/Accuracy/F1.
* **Full CLI runner (`train.py`)** – reproduce your notebook runs head‑less.
* Saved **plots** are dropped into a `plots/` folder **automatically** when using the CLI.

---

## Repository Layout

```
medscan/                  # Package root
├── __init__.py           # Exposes Data, PreprocessConfig, TrainConfig, Pipeline
├── config.py             # @dataclass configs used throughout
├── data.py               # Data.split utility (train/val/test)
├── pipeline.py           # Core training/inference/evaluation pipeline
└── transform.py          # Augmentation & class‑balancing logic
examples/                 # Example notebooks + sample CSV
  └── merged_dataframe.csv
train.py                  # CLI interface wrapping the Pipeline
README.md                 # (this file)
```

---

## Installation

> **Prerequisites**: Python ≥ 3.9

| Target            | Command                     |
| ----------------- | --------------------------- |
| **CPU (default)** | `pip install medscan`       |
| **cu116 build**   | `pip install medscan-cu116` |
| **cu117 build**   | `pip install medscan-cu117` |
| **cu118 build**   | `pip install medscan-cu118` |
| **cu121 build**   | `pip install medscan-cu121` |
| **cu124 build**   | `pip install medscan-cu124` |

> Pick **exactly one** line that matches your CUDA toolkit (or none for CPU). No extra wheels needed — each tag bundles the correct Torch + torchvision wheels.

| Environment     | Requirements file                     |
| --------------- | ------------------------------------- |
| CPU (default)   | `pip install -r requirements_cpu.txt` |
| cu116           | `pip install -r requirements_cu116.txt` |
| cu117           | `pip install -r requirements_cu117.txt` |
| cu118           | `pip install -r requirements_cu118.txt` |
| cu121           | `pip install -r requirements_cu121.txt` |
| cu124           | `pip install -r requirements_cu124.txt` |
> **Tip** – if you already have PyTorch installed, make sure the wheel versions match the list above.

---

## Quick‑Start Notebook

Open `examples/medscan_quickstart.ipynb` and follow the annotated steps — the only manual preparation is to **create a single DataFrame that already contains `img_path` plus all target columns**. Everything afterwards (split, augment, train, evaluate) is automated by the pipeline.

````python
import pandas as pd
from medscan import Pipeline, Data, PreprocessConfig, TrainConfig

# 1️⃣  Prepare your own merged DataFrame -> df_merged (img_path + labels)
# 2️⃣  Split, configure, train, evaluate — see the notebook for details
# ... see the full notebook in /examples for detailed comments
````

The notebook in examples shows how to build a toy `df_merged`, configure preprocessing/training, train for **one epoch**, evaluate, and save the best model.

---

## Command‑Line Usage

`train.py` wraps every step so you can train from the shell – no notebook needed.

### Minimal run

```bash
python train.py \
  --data_path "path/to/merged_dataframe.csv"
```

> This uses *all defaults*: CPU, 70 / 15 / 15 train‑val‑test split, no augmentation, single `resnet34` backbone, multi‑head mode, 10 epochs, and saves plots to `./plots/`.

### Full run (every flag)

```bash
python train.py \
  --data_path "path/to/merged_dataframe.csv" \
  --filter_column "Neuro_Imaging=1" \
  --filter_column "Hemisphere=0" \
  # (repeat --filter_column to apply multiple conditions)
  --targets "Neuro_Imaging,Motion_Artefact,Skull_Visibility,Projection,Contrast_fluid,DSA,Hemisphere,ICA_Top_visible,MCA_visible" \
  --train_frac 0.7  \
  --val_frac 0.15  \
  --seed 123 \
  --augment  \
  --augment_factor 2  \
  --balance_on \
  --augmented_image_path augmented_images \
  --elastic_alpha 34.0  \
  --elastic_sigma 4.0 \
  --contrast_min 0.4 \
  --contrast_max 0.9 \
  --input_size 224 \
  --batch_size 32 \
  --epochs 10 \
  --early_stopping_patience 3 \
  --learning_rate 0.001 \
  --optimizer AdamW \
  --dropout \
  --dropout_rate 0.5 \
  --mixed_precision \
  --save_best_model \
  --checkpoint_dir checkpoints \
  --metric val_loss \
  --metric_mode min \
  --confidence_score \
  --pretrained_models "resnet34,resnet50" \
  --train_per_label \
  --force_cpu \
  --save_model_path best_model.pt \
  --plots "confusion_matrix,loss_vs_epoch,lr_vs_epoch" \
  --metrics "AUC,accuracy,F1"
```

All arguments are documented via `python train.py -h`.

> **Plots** are saved to `./plots/plot_*.png` (auto‑created).
>
> **Augmented images** are saved under the folder you specify in `--augmented_image_path` (default: `augmented_images/`).

---
## Prediction CLI

`predict.py` lets you run inference on a folder of images using a saved pipeline directory or checkpoint.

```bash
python medscan/predict.py \
  --img_dir path/to/image_folder \
  --model_path path/to/pipeline_dir \
  --labels all \
  --output_csv predictions.csv \
  --force_cpu
```

Use `--labels` to restrict targets, `--confidence` to output probability scores, and omit `--force_cpu` to use a GPU if available.

---
## API Overview

| Class / Function                | Role                                                          |
| ------------------------------- | ------------------------------------------------------------- |
| `Data.split`                    | Stratified (or group‑aware) train/val/test split in one call. |
| `PreprocessConfig`              | Declarative augmentation & balance settings.                  |
| `TrainConfig`                   | Training hyper‑parameters, device, backbone list, etc.        |
| `Pipeline`                      | Orchestrates training, prediction, evaluation, save/load.     |
| `transform.augment_and_balance` | Internal helper to expand/upsample data.                      |

All objects live under `medscan` and are re‑exported via `__init__.py` for convenience:

```python
from medscan import Data, PreprocessConfig, TrainConfig, Pipeline
```

---

## Training Pipeline Walk‑Through

1. **Provide a merged DataFrame** — user‑supplied, must include `img_path` and all label columns.
2. **Split** into train / val / test with `Data.split()` (ensures each class appears in every subset).
3. **Preprocess** (`PreprocessConfig`)

   * Optional augmentation: *elastic*, *contrast*, *contrast + elastic*.
   * Optional class balancing (upsampling) – requires `augment=True`.
   * Optional mask handling & context features.
4. **Model training** (`Pipeline.fit`)

   * **Multi‑head** *(default)*: one backbone + multiple classification heads.
   * **Per‑label**: one backbone *per* target (single head each).
   * Early stopping is tracked individually per head.
5. **Prediction** (`Pipeline.predict`): adds `Label_<target>` columns.
6. **Evaluation** (`Pipeline.evaluate`): calculates metrics & shows / saves plots.
7. **Save / Load** (`Pipeline.save` / `Pipeline.load`): preserves all weights and head mapping.

---

## Saving, Loading & Plot Outputs

* **Saving**: `model.save("best_model.pt")` stores either a single multi‑head state or a dict of per‑label states.
* **Loading**: supply the same `PreprocessConfig` / `TrainConfig` (device can differ) and call `model.load()`.
* **Plots**: when run via the CLI, every `plt.show()` call is monkey‑patched to dump PNGs under `plots/`. In notebooks they still display inline.

---

## Troubleshooting

| Issue                       | Fix                                                                      |
| --------------------------- | ------------------------------------------------------------------------ |
| *CUDA library not found*    | Install a matching `requirements_cuXXX.txt` wheel or pass `--force_cpu`. |
| *Class missing in test/val* | Lower `train_frac` / `val_frac` or disable `require_all_classes`.        |
| *No plots on server*        | Use the CLI; plots are saved to disk instead of shown.                   |
| *OOM on GPU*                | Reduce `--batch_size`, `--input_size`, or train on CPU.                  |

---

## License

This project is released under the MIT License. See [LICENSE](LICENSE) for details.