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[project] |
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name = "nnunetv2" |
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version = "2.5" |
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requires-python = ">=3.9" |
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description = "nnU-Net_translation is an adapted nnUNet for medical image translation" |
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readme = "README.md" |
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license = { file = "LICENSE" } |
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authors = [ |
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{ name = "Bowen Xin", email = "bowen.xin@csiro.au"}, |
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] |
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classifiers = [ |
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"Development Status :: 5 - Production/Stable", |
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"Intended Audience :: Developers", |
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"Intended Audience :: Science/Research", |
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"Intended Audience :: Healthcare Industry", |
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"Programming Language :: Python :: 3", |
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"License :: OSI Approved :: Apache Software License", |
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"Topic :: Scientific/Engineering :: Artificial Intelligence", |
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"Topic :: Scientific/Engineering :: Image Recognition", |
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"Topic :: Scientific/Engineering :: Medical Science Apps.", |
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] |
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keywords = [ |
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'deep learning', |
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'image segmentation', |
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'semantic segmentation', |
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'medical image analysis', |
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'medical image segmentation', |
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'nnU-Net', |
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'nnunet', |
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'image translation', |
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'image synthesis', |
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'medical image translation' |
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] |
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dependencies = [ |
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"acvl-utils>=0.2,<0.3", |
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"matplotlib", |
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"seaborn", |
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] |
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[project.urls] |
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homepage = "https://github.com/bowenxin/nnsyn" |
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repository = "https://github.com/bowenxin/nnsyn" |
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[project.scripts] |
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nnUNetv2_plan_and_preprocess = "nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:plan_and_preprocess_entry" |
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nnUNetv2_extract_fingerprint = "nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:extract_fingerprint_entry" |
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nnUNetv2_plan_experiment = "nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:plan_experiment_entry" |
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nnUNetv2_preprocess = "nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:preprocess_entry" |
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nnUNetv2_train = "nnunetv2.run.run_training:run_training_entry" |
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nnUNetv2_unpack = "nnunetv2.run.run_training:run_unpacking_entry" |
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nnUNetv2_predict_from_modelfolder = "nnunetv2.inference.predict_from_raw_data:predict_entry_point_modelfolder" |
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nnUNetv2_predict = "nnunetv2.inference.predict_from_raw_data:predict_entry_point" |
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nnUNetv2_convert_old_nnUNet_dataset = "nnunetv2.dataset_conversion.convert_raw_dataset_from_old_nnunet_format:convert_entry_point" |
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nnUNetv2_find_best_configuration = "nnunetv2.evaluation.find_best_configuration:find_best_configuration_entry_point" |
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nnUNetv2_determine_postprocessing = "nnunetv2.postprocessing.remove_connected_components:entry_point_determine_postprocessing_folder" |
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nnUNetv2_apply_postprocessing = "nnunetv2.postprocessing.remove_connected_components:entry_point_apply_postprocessing" |
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nnUNetv2_ensemble = "nnunetv2.ensembling.ensemble:entry_point_ensemble_folders" |
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nnUNetv2_accumulate_crossval_results = "nnunetv2.evaluation.find_best_configuration:accumulate_crossval_results_entry_point" |
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nnUNetv2_plot_overlay_pngs = "nnunetv2.utilities.overlay_plots:entry_point_generate_overlay" |
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nnUNetv2_download_pretrained_model_by_url = "nnunetv2.model_sharing.entry_points:download_by_url" |
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nnUNetv2_install_pretrained_model_from_zip = "nnunetv2.model_sharing.entry_points:install_from_zip_entry_point" |
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nnUNetv2_export_model_to_zip = "nnunetv2.model_sharing.entry_points:export_pretrained_model_entry" |
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nnUNetv2_move_plans_between_datasets = "nnunetv2.experiment_planning.plans_for_pretraining.move_plans_between_datasets:entry_point_move_plans_between_datasets" |
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nnUNetv2_evaluate_folder = "nnunetv2.evaluation.evaluate_predictions:evaluate_folder_entry_point" |
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nnUNetv2_evaluate_simple = "nnunetv2.evaluation.evaluate_predictions:evaluate_simple_entry_point" |
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nnUNetv2_convert_MSD_dataset = "nnunetv2.dataset_conversion.convert_MSD_dataset:entry_point" |
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nnsyn_plan_and_preprocess = "nnunetv2.nnsyn.nnsyn_preprocessing_entrypoints:nnsyn_plan_and_preprocess_entry" |
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nnsyn_plan_and_preprocess_seg = "nnunetv2.nnsyn.nnsyn_preprocessing_entrypoints:nnsyn_plan_and_preprocess_seg_entry" |
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nnsyn_train = "nnunetv2.run.run_training:run_training_entry" |
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nnsyn_predict = "nnunetv2.nnsyn.nnsyn_predict_entrypoints:nnsyn_predict_entry" |
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[project.optional-dependencies] |
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dev = [ |
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"black", |
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"ruff", |
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"pre-commit" |
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] |
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[build-system] |
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requires = ["setuptools>=67.8.0"] |
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build-backend = "setuptools.build_meta" |
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[tool.codespell] |
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skip = '.git,*.pdf,*.svg' |
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