Simple Deep learning tools for Bio-informatics & Digital Pathology.
- Due to the compatibility issue of the Openslide library, only installation in Ubuntu environment is now supported. We promise to support you on all platforms as soon as possible.
It is not a stable project yet, so there maybe some minor|major bugs. We will provide a complete product in the near future, so we ask for your patience.
python3 -m pip install fake-doctors -U
Now support only Camelyon16 whole slide image dataset(train/test).
- Warning: There is an issue related to the wget library, so debugging is in progress. If it is confirmed that a problem with wget lib itself, we will migrate to a more stable library such as requests.
※ 800GB+ or free storage is required.
In the near future, we will provide a lightweight benchmark dataset of dozens of gigabytes for computer vision research in the field of digital pathology.
from fake_doctors.dataset import Camelyon16
downloader = Camelyon16(urls_dir_in=/path/to/cache/download/urls,
wsi_dir_out=/path/to/save/dataset,
annots_dir_out=/path/to/save/annotations)
# Download training data
downloader.download_trainset()
# Split training/validation data
downloader.split_train_valid(ratio=0.2)
# Download test data
downloader.download_testset()from fake_doctors.annotation import xml_to_json
xml_to_json(xml_path_in=/path/to/xml/annotations,
json_path_out=/path/to/save/json/annotations)from fake_doctors.mask import generate_roi_mask
generate_roi_mask(wsi_path_in=/path/to/dataset,
mask_path_out=/path/to/save/mask)from fake_doctors.annotation import LesionAnnotations
lesion_annots = LesionAnnotations(annot_path=/path/to/annotation)
tumor_coords = lesion_annots.filter_tumor_coords(save_dir='/path/to/save/tumor/coords/list,
points=coordinates,
is_pos=True)from fake_doctors.sampling import PatchSamplers
num_train_patches = 2000000
num_valid_patches = 20000
train_patch_sampler = PatchSampler(wsi_dir_in=/path/to/dataset/dir
masks_dir_in=/path/to/dataset/masks/dir,
annots_dir_in=/path/to/annotations/dir,
tumor_coords_dir_in=/path/to/tumor/coordinates/cache,
normal_coords_dir_in=/path/to/normal/coordinates/cache,
patches_dir_out=/path/to/save/sampled/patches)
valid_patch_sampler = PatchSampler(wsi_dir_in=/path/to/dataset/dir
masks_dir_in=/path/to/dataset/masks/dir,
annots_dir_in=/path/to/annotations/dir,
tumor_coords_dir_in=/path/to/tumor/coordinates/cache,
normal_coords_dir_in=/path/to/normal/coordinates/cache,
patches_dir_out=/path/to/save/sampled/patches)
train_patch_sampler.sample_patches(num_patches=num_train_patches)
valid_patch_sampler.sample_patches(num_patches=num_valid_patches)- Working in progress:
The model used in our project is still being trained. After the model has completely converged, it will be released as soon as the stability of the model is proven, because otherwise, it can confuse users or learners. Since transfer learning cannot be used and the computer power is insufficient, the model is expected to be released in January 2021.
- Working in progress:
The model used in our project is still being trained. After the model has completely converged, it will be released as soon as the stability of the model is proven. Since transfer learning cannot be used and the computer power is insufficient, the model is expected to be released in January 2021.