hocuspocus-ood 0.0.1

HocusPOCUS: An out-of-distribution (OOD) detection dataset for point-of-care ultrasound

HocusPOCUS Collection

⚡ An OOD benchmark for point-of-care ultrasound 🚀 ⚡

---

💴 About the Data

	Description	Train/Val/Test
HP-Atlas	Crowd-sourced POCUS videos from 11 different views	176/217/461 videos
HP-Quality	Pairs of POCUS vs. non-POCUS images	426/520/1154 images
HP-Noise	Generated images with different foreground shapes	480/480/960 images

---

🔧 Installation

(Automatic) Installation:

# Option 1. Available on PyPI
pip install hocuspocus_ood

# Option 2. Local Pip Install
git clone https://github.com/stan-hua/hocuspocus
pip install -e .

🏃 Quickstart

Load HocusPOCUS Datasets:

from hocuspocus_ood import HocusPocusDataset, load_hocuspocus_metadata

# Parameters
dataset_name = "atlas"          # or "quality" or "noise"

# Optional parameters
hparams = {
    "transform": ...                # Torchvision transforms
    "load_image_func": ...,         # Custom calling function to load image given image path
    "load_image_kwargs": ...,       # Keyword arguments for custom image loading function
    "img_mode": 3,                  # 3 = RGB, 1 = Grayscale
    "img_size": (224, 224),         # Image size to resize to
    "scale": True,                  # If True, perform min-max normalization
}

# 1. HP-Atlas dataset               # NOTE: Both the extracted foreground and background are loaded
dataset = HocusPocusDataset("atlas", **hparams)
# 1.1. HP-Atlas dataset with background details kept
# dataset = HocusPocusDataset("atlas", separate_background=False, **hparams)
print(dataset[0])
# {
#   'id': 'pocus_atlas-renal-3-1',
#   'video_id': 'pocus_atlas-renal-3',
#   'img': tensor([[[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0157, 0.1569]]]),
#   'background_img': tensor([[[0.0118, 0.0118, 0.0118,  ..., 0.0118, 0.0078, 0.0000]]]),
#   'label': 'renal',
# }

# 2. HP-Quality dataset
dataset = HocusPocusDataset("quality", **hparams)
print(dataset[0])
# {
#   'id': '0001-0',
#   'img': tensor([[[0.4235, 0.4510, 0.5020,  ..., 0.4627, 0.4471, 0.4549]]]),
#   'label': 'thyroid',
#   'quality': 'low_quality',
# }

# 3. HP-Noise dataset
dataset = HocusPocusDataset("noise", **hparams)
print(dataset[0])
# {
#   'id': 'hp_noise-1',
#   'img': tensor([[[0., 0., 0.,  ..., 0., 0., 0.]]]),
#   'label': 'noise-solid',
# }

🔧 Additional Utilities

A. Transformations

1. SquarePad - zero pads image to 1:1 aspect ratio

from hocuspocus_ood import SquarePad

# Example 1. On image
img = ...
transform = SquarePad()
img_padded = transform(img)

# Example 2. In a composed transform
import torchvision
transforms = torchvision.transforms.Compose([SquarePad(), ...])

B. Augmentations

1. MixBackground - shuffles and overlays batch of foreground and background images (assuming images are the same size)

from hocuspocus_ood import MixBackground

# Example 1. On batches of images
foreground_imgs = ...
background_imgs = ...
transform = MixBackground()
overlayed_imgs = transform(foreground_imgs, background_imgs)

2. IgnoreEmptyRandomResizedCrop - crops in a region where >= 50% of pixels are non-zero

from hocuspocus_ood import IgnoreEmptyRandomResizedCrop

# Example 1. In a composed transform
import torchvision
transforms = torchvision.transforms.Compose([IgnoreEmptyRandomResizedCrop(), ...])

C. Data Samplers

1. ImbalancedDatasetSampler - samples data points equally from each class in each batch

from torch.utils.data import DataLoader
from hocuspocus_ood import ImbalancedDatasetSampler

# Example 1. Given a Dataset object
dataset = ...
sampler = ImbalancedDatasetSampler(dataset)
dataloader = DataLoader(dataset, sampler=sampler, shuffle=False)

2. InfiniteBatchSampler - samples batches infinitely from the dataset

from torch.utils.data import DataLoader
from hocuspocus_ood import ImbalancedDatasetSampler

# Example 1. Given a Dataset object
dataset = ...
batch_size = 32
batch_sampler = InfiniteBatchSampler(dataset, batch_size=batch_size)
dataloader = DataLoader(dataset, batch_sampler=batch_sampler, shuffle=False)

⭐ Advanced

Re-create HocusPOCUS datasets:

from hocuspocus_ood.scripts.create_dataset import *

# (Optional) Change save directory
save_dir = None

# 1. HP-Atlas (scrape from POCUS Atlas then process video)
download_pocus_atlas(save_dir)
process_pocus_atlas_dataset(save_dir, save_dir, extract_background=False)

# 2. HP-Quality (manually download from GrandChallenge - USEnhance 2023)
download_pocus_quality(save_dir)
create_pocus_quality_metadata(save_dir)

# 3. HP-Noise (generate data)
create_pocus_noise_dataset(save_dir)

👏 Acknowledgements

Team Members:

1. Stanley Hua
2. Lauren Erdman

Collaborators:

1. SickKids, Urology Clinic
2. Stanford Health Lucille Packard Children's, Urology Clinic
3. Sinai Health, Maternal-Fetal Clinic
4. Ontario Fetal Centre

Citation

If you find our work useful, please consider citing our paper!

@article{YourName,
  title={Your Title},
  author={Your team},
  journal={Location},
  year={Year}
}
``