Tiatoolbox

Major Updates and Feature Improvements

- Adds nucleus instance segmentation base class
- Adds [HoVerNet](https://www.sciencedirect.com/science/article/abs/pii/S1361841519301045) architecture
- Adds multi-task segmentor [HoVerNet+](https://arxiv.org/abs/2108.13904) model
- Adds <a href="https://www.thelancet.com/journals/landig/article/PIIS2589-7500(2100180-1/fulltext">IDaRS</a> pipeline
- Adds [SlideGraph](https://arxiv.org/abs/2110.06042) pipeline
- Adds PCam patch classification models
- Adds support for stain augmentation feature
- Adds classes and functions under `tiatoolbox.tools.graph` to enable construction of graphs in a format which can be used with PyG (PyTorch Geometric).
- Add classes which act as a mutable mapping (dictionary like) structure and enables efficient management of annotations. (135)
- Adds example notebook for adding advanced models
- Adds classes which can generate zoomify tiles from a WSIReader object.
- Adds WSI viewer using Zoomify/WSIReader API (212)
- Adds README to example page for clarity
- Adds support to override or specify mpp and power

Changes to API

- Replaces `models.controller` API with `models.engine`
- Replaces `CNNPatchPredictor` with `PatchPredictor`

Bug Fixes and Other Changes

- Fixes `filter_coordinates` read wrong resolutions for patch extraction
- For `PatchPredictor`
- `ioconfig` will supersede everything
- if `ioconfig` is not provided
- If `model` is pretrained (defined in `pretrained_model.yaml` )
- Use the yaml ioconfig
- Any other input patch reading arguments will overwrite the yaml ioconfig (at the same keyword).
- If `model` is not defined, all input patch reading arguments must be provided else exception will be thrown.
- Improves performance of mask based patch extraction

Development related changes

- Improve tests performance for Travis runs
- Adds feature detection mechanism to detect the platform and installed packages etc.
- On demand imports for some libraries for performance
- Improves performance of mask based patch extraction

Major Updates and Feature Improvements

- Adds `SemanticSegmentor` which is Predictor equivalent for semantic segmentation.
- Add `TIFFWSIReader` class to support OMETiff reading.
- Adds `FeatureExtractor` API to controller.
- Adds WSI Serialization Dataset which support changing parallel workers on the fly. This would reduce the time spent to create new worker for every WSI/Tile (costly).
- Adds IOState data class to contain IO information for loading input to model and assembling model output back to WSI/Tile.
- Minor updates for `get_coordinates` to pave the way for getting patch IO for segmentation.
- Migrates old code to new variable names (patch extraction, patch wsi model).
- Change in API from `pretrained_weight` to `pretrained_weights`.
- Adds cli for semantic segmentation.
- Update python notebooks to add `read_rect` and `read_bounds` examples with `mpp` read.

Changes to API

- Adds `WSIReader.open`. `get_wsireader` will deprecate in the next release. Please use `WSIReader.open` instead.
- CLI is now POSIX compatible
- Replaces underscores in variable names with hyphens
- Models API updated to use `pretrained_weights` instead of `pretrained_weight`.
- Move string_to_tuple to tiatoolbox/utils/misc.py

Bug Fixes and Other Changes

- Fixes README git clone instructions.
- Fixes stain normalisation due to changes in sklearn.
- Fixes a test in tests/test_slide_info
- Fixes readthedocs documentation issues

Development related changes

- Adds dependencies for tiffile, imagecodecs, zarr.
- Adds more stringent pre-commit checks
- Moved local test files into `tiatoolbox/data`.
- Fixed `Manifest.ini` and added `tiatoolbox/data`. This means that this directory will be downloaded with the package.
- Using `pkg_resources` to properly load bundled resources (e.g. `target_image.png`) in `tiatoolbox.data`.
- Removed duplicate code in `conftest.py` for downloading remote files. This is now in `tiatoolbox.data._fetch_remote_file`.
- Fixes errors raised by new flake8 rules.
- Remove leading underscores from fixtures.
- Rename some remote sample files to make more sense.
- Moves all cli commands/options from cli.py to cli_commands to make it clean and easier to add new commands
- Removes redundant tests
- Updates to new GitHub organisation name in the repo
- Fixes related links

Major and Feature Improvements

- Drops support for python 3.6
- Update minimum requirement to python 3.7
- Adds support for python 3.9
- Adds `models` base to the repository. Currently, PyTorch models are supported. New custom models can be added. The tiatoolbox also supports using custom weights to pre-existing built-in models.
- Adds `classification` package and CNNPatchPredictor which takes predefined model architecture and pre-trained weights as input. The pre-trained weights for classification using kather100k data set is automatically downloaded if no weights are provided as input.
- Adds mask-based patch extraction functionality to extract patches based on the regions that are highlighted in the `input_mask`. If `'auto'` option is selected, a tissue mask is automatically generated for the `input_image` using tiatoolbox `TissueMasker` functionality.
- Adds visualisation module to overlay the results of an algorithm.

Changes to API

- Command line interface for stain normalisation can be called using the keyword `stain-norm` instead of `stainnorm`
- Replaces `FixedWindowPatchExtractor` with `SlidingWindowPatchExtractor` .
- get_patchextractor takes the `slidingwindow` as an argument.
- Depreciates `VariableWindowPatchExtractor`

Bug Fixes and Other Changes

- Significantly improved python notebook documentation for clarity, consistency and ease of use for non-experts.
- Adds detailed installation instructions for Windows, Linux and Mac

Development related changes

- Moves flake8 above pytest in the `travis.yml` script stage.
- Adds `set -e` at the start of the script stage in `travis.yml` to cause it to exit on error and (hopefully) not run later parts of the stage.
- Readthedocs related changes
- Uses `requirements.txt` in `.readthedocs.yml`
- Uses apt-get installation for openjpeg and openslide
- Removes conda build on readthedocs build
- Adds extra checks to pre-commit, e.g., import sorting, spellcheck etc. Detailed list can be found on this [commit](https://github.com/TissueImageAnalytics/tiatoolbox/commit/662a143e915fa55416badd992d8e7358211730a6).

Major and Feature Improvements

- Add `TissueMasker` class to allow tissue masking using `Otsu` and `Morphological` processing.
- Add helper/convenience method to WSIReader(s) to produce a mask. Add reader object to allow reading a mask conveniently as if it were a WSI i.e., use same location and resolution to read tissue area and mask area.
- Add `PointsPatchExtractor` returns patches that can be used by classification models. Takes `csv`, `json` or `pd.DataFrame` and returns patches corresponding to each pixel location.
- Add feature `FixedWindowPatchExtractor` to run sliding window deep learning algorithms.
- Add example notebooks for patch extraction and tissue masking.
- Update readme with improved instructions to use the toolbox. Make the README file somewhat more comprehensible to beginners, particularly those with not much background or experience.

Changes to API

- `tiatoolbox.dataloader` replaced by `tiatoolbox.wsicore`

Bug Fixes and Other Changes

- Minor bug fixes

Development-related changes

- Improve unit test coverage.
- Move test data to tiatoolbox server.

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Bug Fixes and Other Changes

- Fix URL for downloading test JP2 image.
- Update readme with new logo.

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Bug Fixes and Other Changes

- Add `scikit-image` as dependency in `setup.py`
- Update notebooks to add instructions to install dependencies

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