Metadata-Version: 2.1
Name: fake-doctors
Version: 0.0.5.2
Summary: Tools for handling whole slide image
Home-page: https://github.com/timecostslives/fake-doctors
Author: Kim Du Nam
Author-email: timecostslives@gmail.com
License: UNKNOWN
Description: # Project Fake Doctors
        
        **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|majgor bugs.
        We will provide a complete product in the near future, so we ask for your patience.
        
        ## Installation
        ```python3 -m pip install fake-doctors -U```
        
        ## Download Datasets
        
        Now support only Camelyon16 whole slide image dataset(train/test).
        
        - **Warning:** There is an issue related to the [wget](https://pypi.org/project/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](https://requests.readthedocs.io/en/master/).
        
        
        **※ 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.
        
        ```python
        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()
        ```
        
        ## Convert XML annotation to JSON
        
        ```python
        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)
        ```
        
        ## Extract ROI from whole slide image(convert to binary mask)
        
        ```python
        from fake_doctors.mask import generate_roi_mask
        
        generate_roi_mask(wsi_path_in=/path/to/dataset,
                          mask_path_out=/path/to/save/mask)
        ```
        
        ## Filter tumor coordinates from whole slide image
        
        ```python
        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)
        ```
        
        ## Sample train/valid patch images
        
        ```python
        from fake_doctors.sampling import 
        
        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)
        ```
        
        ## Prototyping metastasis classifier model and training
        
        - **Working in progress:**<br>
          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.
          
        ## Load checkpoints
        
        - **Working in progress:**<br>
          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.
        
        
                                                       
        
        
        
        
        
        
        ## 
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.6
Description-Content-Type: text/markdown
