.. Copyright © 2022 Idiap Research Institute <contact@idiap.ch> .. .. SPDX-License-Identifier: GPL-3.0-or-later ===================== Preset Configurations ===================== .. _mednet.libs.classification.config: ------------------------------------ Classification Preset Configurations ------------------------------------ This module contains preset configurations for baseline CNN architectures and DataModules in a classification task. .. _mednet.libs.classification.config.models: Pre-configured Models ^^^^^^^^^^^^^^^^^^^^^ Pre-configured models you can readily use. .. autosummary:: :toctree: api/config.models :template: config.rst mednet.libs.classification.config.models.alexnet mednet.libs.classification.config.models.alexnet_pretrained mednet.libs.classification.config.models.densenet mednet.libs.classification.config.models.densenet_pretrained mednet.libs.classification.config.models.densenet_rs mednet.libs.classification.config.models.logistic_regression mednet.libs.classification.config.models.mlp mednet.libs.classification.config.models.pasa .. _mednet.libs.classification.config.datamodules: DataModule support ^^^^^^^^^^^^^^^^^^ Base DataModules and raw data loaders for the various databases currently supported in this package, for your reference. Each pre-configured DataModule can receive the name of one or more splits as argument to build a fully functional DataModule that can be used in training, prediction or testing. .. autosummary:: :toctree: api/config.datamodules mednet.libs.classification.config.data.hivtb.datamodule mednet.libs.classification.config.data.indian.datamodule mednet.libs.classification.config.data.montgomery.datamodule mednet.libs.classification.config.data.montgomery_shenzhen.datamodule mednet.libs.classification.config.data.montgomery_shenzhen_indian.datamodule mednet.libs.classification.config.data.montgomery_shenzhen_indian_padchest.datamodule mednet.libs.classification.config.data.montgomery_shenzhen_indian_tbx11k.datamodule mednet.libs.classification.config.data.nih_cxr14.datamodule mednet.libs.classification.config.data.nih_cxr14_padchest.datamodule mednet.libs.classification.config.data.padchest.datamodule mednet.libs.classification.config.data.shenzhen.datamodule mednet.libs.classification.config.data.tbpoc.datamodule mednet.libs.classification.config.data.tbx11k.datamodule .. _mednet.libs.classification.config.datamodule-instances: Pre-configured DataModules ^^^^^^^^^^^^^^^^^^^^^^^^^^ DataModules provide access to preset pytorch dataloaders for training, validating, testing and running prediction tasks. Each of the pre-configured DataModule is based on one (or more) of the :ref:`supported base DataModules <mednet.libs.classification.config.datamodules>`. .. autosummary:: :toctree: api/config.datamodule-instances :template: config.rst mednet.libs.classification.config.data.indian.default mednet.libs.classification.config.data.montgomery.default mednet.libs.classification.config.data.montgomery_shenzhen.default mednet.libs.classification.config.data.montgomery_shenzhen_indian.default mednet.libs.classification.config.data.montgomery_shenzhen_indian_padchest.default mednet.libs.classification.config.data.montgomery_shenzhen_indian_tbx11k.v1_healthy_vs_atb mednet.libs.classification.config.data.montgomery_shenzhen_indian_tbx11k.v2_others_vs_atb mednet.libs.classification.config.data.nih_cxr14.default mednet.libs.classification.config.data.nih_cxr14_padchest.idiap mednet.libs.classification.config.data.padchest.idiap mednet.libs.classification.config.data.shenzhen.default mednet.libs.classification.config.data.tbx11k.v1_healthy_vs_atb mednet.libs.classification.config.data.tbx11k.v2_others_vs_atb mednet.libs.classification.config.data.tbx11k.v2_others_vs_atb .. _mednet.libs.classification.config.datamodule-instances.folds: Cross-validation DataModules ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ We support cross-validation with precise preset folds. In this section, you will find the configuration for the first fold (fold-0) for all supported DataModules. Nine other folds are available for every configuration (from 1 to 9), making up 10 folds per supported DataModule. .. autosummary:: :toctree: api/config.datamodule-folds :template: config.rst mednet.libs.classification.config.data.hivtb.fold_0 mednet.libs.classification.config.data.indian.fold_0 mednet.libs.classification.config.data.montgomery.fold_0 mednet.libs.classification.config.data.montgomery_shenzhen.fold_0 mednet.libs.classification.config.data.montgomery_shenzhen_indian.fold_0 mednet.libs.classification.config.data.montgomery_shenzhen_indian_tbx11k.v1_fold_0 mednet.libs.classification.config.data.montgomery_shenzhen_indian_tbx11k.v2_fold_0 mednet.libs.classification.config.data.shenzhen.fold_0 mednet.libs.classification.config.data.tbpoc.fold_0 mednet.libs.classification.config.data.tbx11k.v1_fold_0 mednet.libs.classification.config.data.tbx11k.v2_fold_0 .. _mednet.libs.segmentation.config: ------------------------------------ Classification Preset Configurations ------------------------------------ This module contains preset configurations for baseline CNN architectures and DataModules in a segmentation task. .. _mednet.libs.segmentation.config.models: Pre-configured Models ^^^^^^^^^^^^^^^^^^^^^ Pre-configured models you can readily use. .. autosummary:: :toctree: api/config.models :template: config.rst mednet.libs.segmentation.config.models.driu_bn mednet.libs.segmentation.config.models.driu_od mednet.libs.segmentation.config.models.driu_pix mednet.libs.segmentation.config.models.driu mednet.libs.segmentation.config.models.hed mednet.libs.segmentation.config.models.lwnet mednet.libs.segmentation.config.models.m2unet mednet.libs.segmentation.config.models.unet .. _mednet.libs.segmentation.config.datamodules: DataModule support ^^^^^^^^^^^^^^^^^^ Base DataModules and raw data loaders for the various databases currently supported in this package, for your reference. Each pre-configured DataModule can receive the name of one or more splits as argument to build a fully functional DataModule that can be used in training, prediction or testing. .. autosummary:: :toctree: api/config.datamodules mednet.libs.segmentation.config.data.chasedb1.datamodule mednet.libs.segmentation.config.data.cxr8.datamodule mednet.libs.segmentation.config.data.drhagis.datamodule mednet.libs.segmentation.config.data.drionsdb.datamodule mednet.libs.segmentation.config.data.drishtigs1.datamodule mednet.libs.segmentation.config.data.drive.datamodule mednet.libs.segmentation.config.data.hrf.datamodule mednet.libs.segmentation.config.data.iostar.datamodule mednet.libs.segmentation.config.data.jsrt.datamodule mednet.libs.segmentation.config.data.montgomery.datamodule mednet.libs.segmentation.config.data.refuge.datamodule mednet.libs.segmentation.config.data.rimoner3.datamodule mednet.libs.segmentation.config.data.shenzhen.datamodule mednet.libs.segmentation.config.data.stare.datamodule .. _mednet.libs.segmentation.config.datamodule-instances: Pre-configured DataModules ^^^^^^^^^^^^^^^^^^^^^^^^^^ DataModules provide access to preset pytorch dataloaders for training, validating, testing and running prediction tasks. Each of the pre-configured DataModule is based on one (or more) of the :ref:`supported base DataModules <mednet.libs.segmentation.config.datamodules>`. .. autosummary:: :toctree: api/config.datamodule-instances :template: config.rst mednet.libs.segmentation.config.data.chasedb1.first_annotator mednet.libs.segmentation.config.data.chasedb1.second_annotator mednet.libs.segmentation.config.data.cxr8.default mednet.libs.segmentation.config.data.drhagis.default mednet.libs.segmentation.config.data.drionsdb.expert1 mednet.libs.segmentation.config.data.drionsdb.expert2 mednet.libs.segmentation.config.data.drishtigs1.optic_cup_all mednet.libs.segmentation.config.data.drishtigs1.optic_cup_any mednet.libs.segmentation.config.data.drishtigs1.optic_disc_all mednet.libs.segmentation.config.data.drishtigs1.optic_disc_any mednet.libs.segmentation.config.data.drive.default mednet.libs.segmentation.config.data.drive.drive_2nd mednet.libs.segmentation.config.data.hrf.default mednet.libs.segmentation.config.data.iostar.optic_disc mednet.libs.segmentation.config.data.iostar.vessel mednet.libs.segmentation.config.data.jsrt.default mednet.libs.segmentation.config.data.montgomery.default mednet.libs.segmentation.config.data.refuge.disc mednet.libs.segmentation.config.data.refuge.cup mednet.libs.segmentation.config.data.rimoner3.cup_exp1 mednet.libs.segmentation.config.data.rimoner3.cup_exp2 mednet.libs.segmentation.config.data.rimoner3.disc_exp1 mednet.libs.segmentation.config.data.rimoner3.disc_exp2 mednet.libs.segmentation.config.data.shenzhen.default mednet.libs.segmentation.config.data.stare.ah mednet.libs.segmentation.config.data.stare.vk ------------------ Data Augmentations ------------------ Sequences of data augmentations you can readily use. .. _mednet.libs.common.config.augmentations: .. autosummary:: :toctree: api/config.augmentations :template: config.rst mednet.libs.common.config.augmentations.elastic mednet.libs.common.config.augmentations.affine .. include:: links.rst