-This is the the official repository for the paper: "Foundation Model for Cancer Imaging Biomarkers "
+This is the the official repository for the paper
+
+
+
-
+
+
+
+
+
+
+
+
-Suraj Pai, Dennis Bontempi, Ibrahim Hadzic, Vasco Prudente, Mateo Sokač, Tafadzwa L. Chaunzwa, Simon Bernatz, Ahmed Hosny, Raymond H Mak, Nicolai J Birkbak, Hugo JWL Aerts
-
-
[![Build Status](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/actions/workflows/build.yml/badge.svg)](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/actions/workflows/build.yml)
[![Python Version](https://img.shields.io/pypi/pyversions/foundation-cancer-image-biomarker.svg)](https://pypi.org/project/foundation-cancer-image-biomarker/)
[![Dependencies Status](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)
@@ -23,7 +30,7 @@ This is the the official repository for the paper: "Foundation Model for Canc
---
-**NOTE: **
+**NOTE:**
For detailed documentation check our [website](https://aim-harvard.github.io/foundation-cancer-image-biomarker/)
---
\ No newline at end of file
diff --git a/docs/assets/Mhub_image.png b/docs/assets/Mhub_image.png
new file mode 100644
index 0000000..6cb0fcd
Binary files /dev/null and b/docs/assets/Mhub_image.png differ
diff --git a/docs/assets/Mhub_image2.png b/docs/assets/Mhub_image2.png
new file mode 100644
index 0000000..7ae09bc
Binary files /dev/null and b/docs/assets/Mhub_image2.png differ
diff --git a/docs/assets/readpaper_logo.png b/docs/assets/readpaper_logo.png
new file mode 100644
index 0000000..5385bab
Binary files /dev/null and b/docs/assets/readpaper_logo.png differ
diff --git a/docs/index.md b/docs/index.md
index 48a5677..04c6e7e 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -3,14 +3,13 @@ hide:
- title
---
#
-
-
-This is the the official documentation for the paper: "Foundation Model for Cancer Imaging Biomarkers "
-
-
-
-
-Suraj Pai, Dennis Bontempi, Ibrahim Hadzic, Vasco Prudente, Mateo Sokač, Tafadzwa L. Chaunzwa, Simon Bernatz, Ahmed Hosny, Raymond H Mak, Nicolai J Birkbak, Hugo JWL Aerts
+
+
+
+
+
+
+
## Documentation Walkthrough
@@ -20,11 +19,11 @@ This is the the official documentation for the paper: "Foundation Model for C
!!! note
[We also provide quickstart examples that run in a free-cloud based environment](./getting-started/cloud-quick-start.md) (through Google Colab) so you can get familiar with our workflows, without having to download anything on your local machine!!
-[Replication Guide](./user-guide/data.md) If you would like to pre-train a foundation model on your own unannotated data or would like to replicate the training and evaluation from our study, see here.
+[Replication Guide](./replication-guide/data.md) If you would like to pre-train a foundation model on your own unannotated data or would like to replicate the training and evaluation from our study, see here.
[Tutorials](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/tree/master/tutorials) We provide comprehensive tutorials that use the foundation model for cancer imaging biomarkers and compare against other popularly used methods. If you would like to build your own study using our foundation model, these set of tutorials are highly recommended as the starting point.
-[API Docs](./api_docs/fmcib/index.html) This is for the more advanced user who would like to deep-dive into different methods and classes provided by our package.
+[API Docs](./reference/run) This is for the more advanced user who would like to deep-dive into different methods and classes provided by our package.
## License
diff --git a/docs/user-guide/analysis.md b/docs/replication-guide/analysis.md
similarity index 100%
rename from docs/user-guide/analysis.md
rename to docs/replication-guide/analysis.md
diff --git a/docs/replication-guide/baselines.md b/docs/replication-guide/baselines.md
new file mode 100644
index 0000000..99d08f0
--- /dev/null
+++ b/docs/replication-guide/baselines.md
@@ -0,0 +1,2 @@
+# Reproduce Baselines
+:hourglass_flowing_sand: Coming soon! :hourglass_flowing_sand:
diff --git a/docs/user-guide/data.md b/docs/replication-guide/data.md
similarity index 98%
rename from docs/user-guide/data.md
rename to docs/replication-guide/data.md
index b509a3c..b220b59 100644
--- a/docs/user-guide/data.md
+++ b/docs/replication-guide/data.md
@@ -62,7 +62,7 @@ bash luna16.sh
The easiest way to download the LUNG1 and RADIO datasets is through s5cmd and [IDC manifests](https://learn.canceridc.dev/data/downloading-data)
For convenience, the manifests for each of the already been provided in `data/download` under `nsclc_radiomics.csv` for LUNG1 and `nsclc_radiogenomics.`
-First, you'll need to install `s5cmd`. Follow the instructions here: https://github.com/peak/s5cmd?tab=readme-ov-file#installation
+First, you'll need to install `s5cmd`. Follow the instructions [here]https://github.com/peak/s5cmd?tab=readme-ov-file#installation
Once you have s5cmd installed, run
diff --git a/docs/user-guide/download_models.md b/docs/replication-guide/download_models.md
similarity index 100%
rename from docs/user-guide/download_models.md
rename to docs/replication-guide/download_models.md
diff --git a/docs/user-guide/fm_adaptation.md b/docs/replication-guide/fm_adaptation.md
similarity index 100%
rename from docs/user-guide/fm_adaptation.md
rename to docs/replication-guide/fm_adaptation.md
diff --git a/docs/user-guide/inference.md b/docs/replication-guide/inference.md
similarity index 100%
rename from docs/user-guide/inference.md
rename to docs/replication-guide/inference.md
diff --git a/docs/user-guide/reproduce_fm.md b/docs/replication-guide/reproduce_fm.md
similarity index 100%
rename from docs/user-guide/reproduce_fm.md
rename to docs/replication-guide/reproduce_fm.md
diff --git a/docs/user-guide/reproduce_baselines.md b/docs/user-guide/reproduce_baselines.md
deleted file mode 100644
index 1372cc3..0000000
--- a/docs/user-guide/reproduce_baselines.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Reproduce Baselines
-
-### Reproducing our baselines
-
-We have several different baselines that we compare against in this study.
-
-
-As mentioned in [section](#supervised-models), we have three different supervised training implementations. Similar to the foundation pre-training, we use YAML files to maintain the configurations of these implementations.
-
-
- Supervised model trained from random initialization
-
-In order to reproduce this training, you can inspect the YAML configuration at `experiments/baselines/supervised_training/supervised_random_init.yaml`. By default, we configure this for Task 1. You can adapt this for Task 2 and Task 3 by searching for 'Note: ' comments in the YAML that outline what must be changed.
-
-You can start training by running this in the root code folder,
-```bash
-lighter fit --config_file ./experiments/baselines/supervised_training/supervised_random_init.yaml
-```
-
-
-
- Fine-tuning a trained supervised model
-
-The YAML configuration at `experiments/baselines/supervised_training/supervised_finetune.yaml` describes how you can fine-tune an already trained supervised model. Note that this is possible only for Task 2 and Task 3 as we used the supervised model trained in Task 1 to load weights from. Make sure you download the weights for Task 1 supervised models. You can follow instructions [here](#model)
-
-
-You can start training by running this in the root code folder,
-```bash
-lighter fit --config_file ./experiments/baselines/supervised_training/supervised_finetune.yaml
-```
-
-
-
-### Reproducing our linear evaluation (Logistic Regression)
diff --git a/fmcib/visualization/verify_io.py b/fmcib/visualization/verify_io.py
index b5f799f..a45f9fe 100644
--- a/fmcib/visualization/verify_io.py
+++ b/fmcib/visualization/verify_io.py
@@ -17,14 +17,18 @@ def visualize_seed_point(row):
None
"""
# Define the transformation pipeline
+ is_label_provided = "label_path" in row
+ keys = ["image_path", "label_path"] if is_label_provided else ["image_path"]
+ all_keys = keys if is_label_provided else ["image_path", "coordX", "coordY", "coordZ"]
+
T = monai_transforms.Compose(
[
- monai_transforms.LoadImaged(keys=["image_path"], image_only=True, reader="ITKReader"),
- monai_transforms.EnsureChannelFirstd(keys=["image_path"]),
- monai_transforms.Spacingd(keys=["image_path"], pixdim=1, mode="bilinear", align_corners=True, diagonal=True),
+ monai_transforms.LoadImaged(keys=keys, image_only=True, reader="ITKReader"),
+ monai_transforms.EnsureChannelFirstd(keys=keys),
+ monai_transforms.Spacingd(keys=keys, pixdim=1, mode="bilinear", align_corners=True, diagonal=True),
monai_transforms.ScaleIntensityRanged(keys=["image_path"], a_min=-1024, a_max=3072, b_min=0, b_max=1, clip=True),
- monai_transforms.Orientationd(keys=["image_path"], axcodes="LPS"),
- monai_transforms.SelectItemsd(keys=["image_path", "coordX", "coordY", "coordZ"]),
+ monai_transforms.Orientationd(keys=keys, axcodes="LPS"),
+ monai_transforms.SelectItemsd(keys=all_keys),
]
)
@@ -32,30 +36,35 @@ def visualize_seed_point(row):
out = T(row)
# Calculate the center of the image
- center = (-out["coordX"], -out["coordY"], out["coordZ"])
- center = np.linalg.inv(np.array(out["image_path"].affine)) @ np.array(center + (1,))
- center = [int(x) for x in center[:3]]
-
- # Define the image and label
image = out["image_path"]
- label = torch.zeros_like(image)
-
- # Define the dimensions of the image and the patch
- C, H, W, D = image.shape
- Ph, Pw, Pd = 50, 50, 50
-
- # Calculate and clamp the ranges for cropping
- min_h, max_h = max(center[0] - Ph // 2, 0), min(center[0] + Ph // 2, H)
- min_w, max_w = max(center[1] - Pw // 2, 0), min(center[1] + Pw // 2, W)
- min_d, max_d = max(center[2] - Pd // 2, 0), min(center[2] + Pd // 2, D)
-
- # Check if coordinates are valid
- assert min_h < max_h, "Invalid coordinates: min_h >= max_h"
- assert min_w < max_w, "Invalid coordinates: min_w >= max_w"
- assert min_d < max_d, "Invalid coordinates: min_d >= max_d"
-
- # Define the label for the cropped region
- label[:, min_h:max_h, min_w:max_w, min_d:max_d] = 1
+ if not is_label_provided:
+ center = (-out["coordX"], -out["coordY"], out["coordZ"])
+ center = np.linalg.inv(np.array(out["image_path"].affine)) @ np.array(center + (1,))
+ center = [int(x) for x in center[:3]]
+
+ # Define the image and label
+ label = torch.zeros_like(image)
+
+ # Define the dimensions of the image and the patch
+ C, H, W, D = image.shape
+ Ph, Pw, Pd = 50, 50, 50
+
+ # Calculate and clamp the ranges for cropping
+ min_h, max_h = max(center[0] - Ph // 2, 0), min(center[0] + Ph // 2, H)
+ min_w, max_w = max(center[1] - Pw // 2, 0), min(center[1] + Pw // 2, W)
+ min_d, max_d = max(center[2] - Pd // 2, 0), min(center[2] + Pd // 2, D)
+
+ # Check if coordinates are valid
+ assert min_h < max_h, "Invalid coordinates: min_h >= max_h"
+ assert min_w < max_w, "Invalid coordinates: min_w >= max_w"
+ assert min_d < max_d, "Invalid coordinates: min_d >= max_d"
+
+ # Define the label for the cropped region
+ label[:, min_h:max_h, min_w:max_w, min_d:max_d] = 1
+ else:
+ label = out["label_path"]
+ center = torch.nonzero(label).float().mean(dim=0)
+ center = [int(x) for x in center][1:]
# Blend the image and the label
ret = blend_images(image=image, label=label, alpha=0.3, cmap="hsv", rescale_arrays=False)
diff --git a/mkdocs.yml b/mkdocs.yml
index 065c13f..c6b1668 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -39,7 +39,8 @@ plugins:
docstring_style: google
options:
# Removed the default filter that excludes private members (that is, members whose names start with a single underscore).
- filters: null
+ filters: null
+ show_source: true
nav:
- 'index.md'
@@ -48,12 +49,13 @@ nav:
- 'Cloud Quick Start': 'getting-started/cloud-quick-start.md'
- 'Quick Start': 'getting-started/quick-start.md'
- 'Replication Guide':
- - 'Data Download and Preprocessing': 'user-guide/data.md'
- - 'Pre-training the FM': 'user-guide/reproduce_fm.md'
- - 'Adapt the FM to downstream tasks': 'user-guide/fm_adaptation.md'
- - 'Extracting Features & Predictions': 'user-guide/inference.md'
- - 'Reproduce Analysis': 'user-guide/analysis.md'
- # - 'Training baselines': 'user-guide/reproduce_baselines.md'
+ - 'Data Download and Preprocessing': 'replication-guide/data.md'
+ - 'Pre-training the FM': 'replication-guide/reproduce_fm.md'
+ - 'Adapt the FM to downstream tasks': 'replication-guide/fm_adaptation.md'
+ - 'Baselines for downstream tasks': 'replication-guide/baselines.md'
+ - 'Extracting Features & Predictions': 'replication-guide/inference.md'
+ - 'Reproduce Analysis': 'replication-guide/analysis.md'
+ # - 'Training baselines': 'replication-guide/reproduce_baselines.md'
- 'Tutorials': https://github.com/AIM-Harvard/foundation-cancer-image-biomarker/tree/master/tutorials
- 'API Reference': 'reference/'
diff --git a/scripts/generate_api_reference_pages.py b/scripts/generate_api_reference_pages.py
index 98d6ec9..9e7f000 100644
--- a/scripts/generate_api_reference_pages.py
+++ b/scripts/generate_api_reference_pages.py
@@ -18,7 +18,10 @@
root = Path(__file__).parent.parent
src = root / PACKAGE
-for path in sorted(src.rglob("*.py")):
+# Sort files by depth
+paths = sorted(src.rglob("*.py"), key=lambda path: len(path.parts))
+
+for path in paths:
print(f"Processing {path}")
module_path = path.relative_to(src).with_suffix("")
diff --git a/tutorials/get_seed_from_mask.ipynb b/tutorials/get_seed_from_mask.ipynb
new file mode 100644
index 0000000..90166fe
--- /dev/null
+++ b/tutorials/get_seed_from_mask.ipynb
@@ -0,0 +1,233 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Extract CoM seed point from Segmentation Masks\n",
+ "\n",
+ "The FMCIB `get_features` function expects image paths and seed points. If you have segmentations masks you would like to convert to CoM, this notebook provides instructions on how this can be achieved. \n",
+ "\n",
+ "Alternatively, you can use our Mhub https://mhub.ai/models/fmcib_radiomics implementation and use the `nrrd_mask_workflow` as mentioned here: https://github.com/MHubAI/documentation/blob/main/documentation/mhub/run_mhub.md#specify-the-workflow\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import SimpleITK as sitk\n",
+ "from fmcib.utils import download_LUNG1, build_image_seed_dict\n",
+ "from fmcib.visualization import visualize_seed_point"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we download a sample from LUNG1 to show the process of centroid extraction. Use your own data here and skip this step. \n",
+ "\n",
+ "The donwload and conversion will take about a minute. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "download_LUNG1(\"dummy\", samples=1)\n",
+ "build_image_seed_dict(\"dummy\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we get the path to the image and mask. This can be nii.gz, nrrd, mha or other formats supported by MONAI's ITKReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pathlib\n",
+ "\n",
+ "dummy_path = pathlib.Path(\"dummy\")\n",
+ "image_path = list(dummy_path.rglob(\"image.nii.gz\"))[0]\n",
+ "mask_path = list(dummy_path.rglob(\"*GTV-1.nii.gz\"))[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "row = {\"image_path\": image_path, \"label_path\": mask_path}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The visualize seed point utility function also visualizes masks when `label_path` is provided as a key in the dict. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "