V1.6.0 dev

examples/local.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+
+__doc__ = """
+Simple example script showing how to use the Sybil library locally to predict risk scores for a set of DICOM files.
+"""
+
+import sybil
+from sybil import visualize_attentions
+
+from utils import get_demo_data
+
+
+def main():
+    # Load a trained model
+    model = sybil.Sybil("sybil_ensemble")
+
+    dicom_files = get_demo_data()
+
+    # Get risk scores
+    serie = sybil.Serie(dicom_files)
+    print(f"Processing {len(dicom_files)} DICOM files")
+    prediction = model.predict([serie], return_attentions=True)
+    scores = prediction.scores
+
+    print(f"Risk scores: {scores}")
+
+    # Visualize attention maps
+    output_dir = "sybil_attention_output"
+
+    print(f"Writing attention images to {output_dir}")
+    series_with_attention = visualize_attentions(
+        serie,
+        attentions=prediction.attentions,
+        save_directory=output_dir,
+        gain=3,
+    )
+
+    print(f"Finished writing attention images to {output_dir}")
+
+if __name__ == "__main__":
+    main()

examples/remote_ark.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+
+__doc__ = """
+This example shows how to use a client to access a 
+remote Sybil server (running Ark) to predict risk scores for a set of DICOM files.
+
+The server must be started separately.
+
+https://github.com/reginabarzilaygroup/Sybil/wiki
+https://github.com/reginabarzilaygroup/ark/wiki
+"""
+import json
+import os
+
+import numpy as np
+import requests
+
+import sybil.utils.visualization
+
+from utils import get_demo_data
+
+if __name__ == "__main__":
+
+    dicom_files = get_demo_data()
+    serie = sybil.Serie(dicom_files)
+
+    # Set the URL of the remote Sybil server
+    ark_hostname = "localhost"
+    ark_port = 5000
+
+    # Set the URL of the remote Sybil server
+    ark_host = f"http://{ark_hostname}:{ark_port}"
+
+    data_dict = {"return_attentions": True}
+    payload = {"data": json.dumps(data_dict)}
+
+    # Check if the server is running and reachable
+    resp = requests.get(f"{ark_host}/info")
+    if resp.status_code != 200:
+        raise ValueError(f"Failed to connect to ARK server. Status code: {resp.status_code}")
+
+    info_data = resp.json()["data"]
+    assert info_data["modelName"].lower() == "sybil", "The ARK server is not running Sybil"
+    print(f"ARK server info: {info_data}")
+
+    # Submit prediction to ARK server.
+    files = [('dicom', open(file_path, 'rb')) for file_path in dicom_files]
+    r = requests.post(f"{ark_host}/dicom/files", files=files, data=payload)
+    _ = [f[1].close() for f in files]
+    if r.status_code != 200:
+        raise ValueError(f"Error occurred processing DICOM files. Status code: {r.status_code}.\n{r.text}")
+
+    r_json = r.json()
+    predictions = r_json["data"]["predictions"]
+
+    scores = predictions[0]
+    print(f"Risk scores: {scores}")
+
+    attentions = predictions[1]
+    attentions = np.array(attentions)
+    print(f"Ark received attention shape: {attentions.shape}")
+
+    # Visualize attention maps
+    save_directory = "remote_ark_sybil_attention_output"
+
+    print(f"Writing attention images to {save_directory}")
+
+    images = serie.get_raw_images()
+    overlayed_images = sybil.utils.visualization.build_overlayed_images(images, attentions, gain=3)
+
+    if save_directory is not None:
+        serie_idx = 0
+        save_path = os.path.join(save_directory, f"serie_{serie_idx}")
+        sybil.utils.visualization.save_images(overlayed_images, save_path, f"serie_{serie_idx}")
+
+    print(f"Finished writing attention images to {save_directory}")
+

examples/utils.py

@@ -0,0 +1,42 @@
+import os
+from urllib.request import urlopen
+
+
+def download_file(url, filepath):
+    response = urlopen(url)
+
+    target_dir = os.path.dirname(filepath)
+    if target_dir and not os.path.exists(target_dir):
+        os.makedirs(target_dir)
+
+    # Check if the request was successful
+    if response.status == 200:
+        with open(filepath, 'wb') as f:
+            f.write(response.read())
+    else:
+        print(f"Failed to download file. Status code: {response.status_code}")
+
+    return filepath
+
+def get_demo_data():
+    demo_data_url = "https://www.dropbox.com/scl/fi/covbvo6f547kak4em3cjd/sybil_example.zip?rlkey=7a13nhlc9uwga9x7pmtk1cf1c&st=dqi0cf9k&dl=1"
+
+    zip_file_name = "sybil_example.zip"
+    cache_dir = os.path.expanduser("~/.sybil")
+    zip_file_path = os.path.join(cache_dir, zip_file_name)
+    os.makedirs(cache_dir, exist_ok=True)
+    if not os.path.exists(zip_file_path):
+        print(f"Downloading demo data to {zip_file_path}")
+        download_file(demo_data_url, zip_file_path)
+
+    demo_data_dir = os.path.join(cache_dir, "sybil_example")
+    image_data_dir = os.path.join(demo_data_dir, "sybil_demo_data")
+    if not os.path.exists(demo_data_dir):
+        print(f"Extracting demo data to {demo_data_dir}")
+        import zipfile
+        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+            zip_ref.extractall(demo_data_dir)
+
+    dicom_files = os.listdir(image_data_dir)
+    dicom_files = [os.path.join(image_data_dir, x) for x in dicom_files]
+    return dicom_files

setup.cfg

@@ -38,14 +38,13 @@ python_requires = >=3.8,<3.11
 # For more information, check out https://semver.org/.
 install_requires =
     importlib-metadata; python_version>="3.8"
-    albumentations==1.1.0
+    imageio==2.34.1
     numpy==1.24.1
     opencv-python==4.5.4.60
     opencv-python-headless==4.5.4.60
     pillow>=10.2.0
     pydicom==2.3.0
     pylibjpeg[all]==2.0.0
-    scikit-learn==1.0.2
     torch==1.13.1+cu117; platform_machine == "x86_64"
     torch==1.13.1; platform_machine != "x86_64"
     torchio==0.18.74
@@ -68,8 +67,10 @@ testing =
     mypy
     black
 train =
+    albumentations==1.1.0
     lifelines==0.26.4
     pytorch_lightning==1.6.0
+    scikit-learn==1.0.2
 
 [options.entry_points]
 console_scripts =

sybil/__init__.py

@@ -19,7 +19,7 @@
 
 from sybil.model import Sybil
 from sybil.serie import Serie
-from sybil.utils.visualization import visualize_attentions
+from sybil.utils.visualization import visualize_attentions, collate_attentions
 import sybil.utils.logging_utils
 
-__all__ = ["Sybil", "Serie", "visualize_attentions", "__version__"]
+__all__ = ["Sybil", "Serie", "visualize_attentions", "collate_attentions", "__version__"]

sybil/augmentations.py

@@ -1,12 +1,18 @@
+import cv2
 import torch
 import torchvision
-import albumentations as A
-from albumentations.pytorch import ToTensorV2
+
 from typing import Literal
 from abc import ABCMeta, abstractmethod
 import numpy as np
 import random
 
+try:
+    import albumentations as A
+except ImportError:
+    # albumentations is not installed, training with augmentations will not be possible
+    A = None
+
 
 def get_augmentations(split: Literal["train", "dev", "test"], args):
     if split == "train":
@@ -94,7 +100,6 @@ class ToTensor(Abstract_augmentation):
 
     def __init__(self):
         super(ToTensor, self).__init__()
-        self.transform = ToTensorV2()
         self.name = "totensor"
 
     def __call__(self, input_dict, sample=None):
@@ -104,6 +109,20 @@ def __call__(self, input_dict, sample=None):
         return input_dict
 
 
+class ResizeTransform:
+    def __init__(self, width, height):
+        self.width = width
+        self.height = height
+
+    def __call__(self, image=None, mask=None):
+        out = {"image": None, "mask": None}
+        if image is not None:
+            out["image"] = cv2.resize(image, dsize=(self.width, self.height), interpolation=cv2.INTER_LINEAR)
+        if mask is not None:
+            out["mask"] = cv2.resize(mask, dsize=(self.width, self.height), interpolation=cv2.INTER_NEAREST)
+        return out
+
+
 class Scale_2d(Abstract_augmentation):
     """
     Given PIL image, enforce its some set size
@@ -115,7 +134,7 @@ def __init__(self, args, kwargs):
         assert len(kwargs.keys()) == 0
         width, height = args.img_size
         self.set_cachable(width, height)
-        self.transform = A.Resize(height, width)
+        self.transform = ResizeTransform(width, height)
 
     def __call__(self, input_dict, sample=None):
         out = self.transform(
@@ -138,6 +157,7 @@ def __init__(self, args, kwargs):
         super(Rotate_Range, self).__init__()
         assert len(kwargs.keys()) == 1
         self.max_angle = int(kwargs["deg"])
+        assert A is not None, "albumentations is not installed"
         self.transform = A.Rotate(limit=self.max_angle, p=0.5)
 
     def __call__(self, input_dict, sample=None):

sybil/model.py

@@ -13,7 +13,6 @@
 from sybil.models.calibrator import SimpleClassifierGroup
 from sybil.utils.logging_utils import get_logger
 from sybil.utils.device_utils import get_default_device, get_most_free_gpu, get_device_mem_info
-from sybil.utils.metrics import get_survival_metrics
 
 
 # Leaving this here for a bit; these are IDs to download the models from Google Drive
@@ -67,7 +66,7 @@
     },
 }
 
-CHECKPOINT_URL = os.getenv("SYBIL_CHECKPOINT_URL", "https://www.dropbox.com/scl/fi/45rtadfdci0bj8dbpotmr/sybil_checkpoints_v1.5.0.zip?rlkey=n8n7pvhb89pjoxgvm90mtbtuk&dl=1")
+CHECKPOINT_URL = os.getenv("SYBIL_CHECKPOINT_URL", "https://github.com/reginabarzilaygroup/Sybil/releases/download/v1.5.0/sybil_checkpoints.zip")
 
 
 class Prediction(NamedTuple):
@@ -107,12 +106,12 @@ def download_sybil(name, cache) -> Tuple[List[str], str]:
     return download_model_paths, download_calib_path
 
 
-def download_and_extract(remote_model_url: str, local_model_dir) -> List[str]:
-    resp = urlopen(remote_model_url)
-    os.makedirs(local_model_dir, exist_ok=True)
+def download_and_extract(remote_url: str, local_dir: str) -> List[str]:
+    os.makedirs(local_dir, exist_ok=True)
+    resp = urlopen(remote_url)
     with ZipFile(BytesIO(resp.read())) as zip_file:
         all_files_and_dirs = zip_file.namelist()
-        zip_file.extractall(local_model_dir)
+        zip_file.extractall(local_dir)
     return all_files_and_dirs
 
 
@@ -379,6 +378,7 @@ def evaluate(
             Output evaluation. See details for :class:`~sybil.model.Evaluation`.
 
         """
+        from sybil.utils.metrics import get_survival_metrics
         if isinstance(series, Serie):
             series = [series]
         elif not isinstance(series, list):