[doc] Add segmentation configs in documentation

doc/api.rst

@@ -209,10 +209,17 @@ CNN and other models implemented.
 .. autosummary::
    :toctree: api/models
 
+   mednet.libs.segmentation.models.driu_bn
+   mednet.libs.segmentation.models.driu_od
+   mednet.libs.segmentation.models.driu_pix
+   mednet.libs.segmentation.models.driu
+   mednet.libs.segmentation.models.hed
    mednet.libs.segmentation.models.losses
    mednet.libs.segmentation.models.lwnet
+   mednet.libs.segmentation.models.m2unet
    mednet.libs.segmentation.models.separate
    mednet.libs.segmentation.models.typing
+   mednet.libs.segmentation.models.unet
 
 
 .. _mednet.libs.segmentation.api.utils:

doc/config.rst

@@ -2,19 +2,25 @@
 ..
 .. SPDX-License-Identifier: GPL-3.0-or-later
 
+=====================
+Preset Configurations
+=====================
+
+
 .. _mednet.libs.classification.config:
 
-Preset Configurations
----------------------
+------------------------------------
+Classification Preset Configurations
+------------------------------------
 
 This module contains preset configurations for baseline CNN architectures and
-DataModules.
+DataModules in a classification task.
 
 
 .. _mednet.libs.classification.config.models:
 
 Pre-configured Models
-=====================
+^^^^^^^^^^^^^^^^^^^^^
 
 Pre-configured models you can readily use.
 
@@ -32,32 +38,16 @@ Pre-configured models you can readily use.
    mednet.libs.classification.config.models.pasa
 
 
-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
-
 .. _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.
 
-.. _mednet.config.datamodules:
-
 .. autosummary::
    :toctree: api/config.datamodules
 
@@ -79,7 +69,7 @@ functional DataModule that can be used in training, prediction or testing.
 .. _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
@@ -109,7 +99,7 @@ DataModule is based on one (or more) of the :ref:`supported base DataModules
 .. _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
@@ -134,4 +124,123 @@ DataModules.  Nine other folds are available for every configuration (from 1 to
    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

doc/install.rst

@@ -294,4 +294,221 @@ Please contact the authors of these databases to have access to the data.
      - 243
 
 
+.. _mednet.libs.segmentation.setup.databases.retinography:
+
+Retinography
+------------
+
+
+.. list-table:: Supported Retinography Datasets (``*``: provided within this package)
+
+   * - Dataset
+     - Reference
+     - H x W
+     - Samples
+     - Mask
+     - Vessel
+     - OD
+     - Cup
+     - Split Reference
+     - Train
+     - Test
+   * - DRIVE_
+     - [DRIVE-2004]_
+     - 584 x 565
+     - 40
+     - ``x``
+     - ``x``
+     -
+     -
+     - [DRIVE-2004]_
+     - 20
+     - 20
+   * - STARE_
+     - [STARE-2000]_
+     - 605 x 700
+     - 20
+     - ``*``
+     - ``x``
+     -
+     -
+     - [MANINIS-2016]_
+     - 10
+     - 10
+   * - CHASE-DB1_
+     - [CHASEDB1-2012]_
+     - 960 x 999
+     - 28
+     - ``*``
+     - ``x``
+     -
+     -
+     - [CHASEDB1-2012]_
+     - 8
+     - 20
+   * - HRF_
+     - [HRF-2013]_
+     - 2336 x 3504
+     - 45
+     - ``x``
+     - ``x``
+     -
+     -
+     - [ORLANDO-2017]_
+     - 15
+     - 30
+   * - IOSTAR_
+     - [IOSTAR-2016]_
+     - 1024 x 1024
+     - 30
+     - ``x``
+     - ``x``
+     - ``x``
+     -
+     - [MEYER-2017]_
+     - 20
+     - 10
+   * - DRIONS-DB_
+     - [DRIONSDB-2008]_
+     - 400 x 600
+     - 110
+     -
+     -
+     - ``x``
+     -
+     - [MANINIS-2016]_
+     - 60
+     - 50
+   * - `RIM-ONE r3`_
+     - [RIMONER3-2015]_
+     - 1424 x 1072
+     - 159
+     -
+     -
+     - ``x``
+     - ``x``
+     - [MANINIS-2016]_
+     - 99
+     - 60
+   * - Drishti-GS1_
+     - [DRISHTIGS1-2014]_
+     - varying
+     - 101
+     -
+     -
+     - ``x``
+     - ``x``
+     - [DRISHTIGS1-2014]_
+     - 50
+     - 51
+   * - REFUGE_
+     - [REFUGE-2018]_
+     - 2056 x 2124 (1634 x 1634)
+     - 1200
+     -
+     -
+     - ``x``
+     - ``x``
+     - [REFUGE-2018]_
+     - 400 (+400)
+     - 400
+   * - DRHAGIS_
+     - [DRHAGIS-2017]_
+     - Varying
+     - 39
+     - ``x``
+     - ``x``
+     -
+     -
+     - [DRHAGIS-2017]_
+     - 19
+     - 20
+
+.. warning:: **REFUGE Dataset Support**
+
+  The original directory ``Training400/AMD`` in REFUGE is considered to be
+  replaced by an updated version provided by the `AMD Grand-Challenge`_ (with
+  matching names).
+
+  The changes concerns images ``A0012.jpg``, which was corrupted in REFUGE, and
+  ``A0013.jpg``, which only exists in the AMD Grand-Challenge version.
+
+
+.. _mednet.libs.segmentation.setup.databases.xray:
+
+X-Ray
+-----
+
+.. list-table:: Supported X-Ray Datasets
+
+   * - Dataset
+     - Reference
+     - H x W
+     - Radiography Type
+     - Samples
+     - Mask
+     - Split Reference
+     - Train
+     - Test
+   * - `Montgomery County`_
+     - [MC-2014]_
+     - 4020 x 4892, or 4892 x 4020
+     - Digital Radiography (DR)
+     - 138
+     - ``*``
+     - [GAAL-2020]_
+     - 96 (+14)
+     - 28
+   * - JSRT_
+     - [JSRT-2000]_
+     - 2048 x 2048
+     - Digitized Radiography (laser digitizer)
+     - 247
+     - ``*``
+     - [GAAL-2020]_
+     - 172 (+25)
+     - 50
+   * - Shenzhen_
+     - [SHENZHEN-2014]_
+     - Varying
+     - Computed Radiography (CR)
+     - 662
+     - ``*``
+     - [GAAL-2020]_
+     - 396 (+56)
+     - 114
+   * - CXR8_
+     - [CXR8-2017]_
+     - 1024 x 1024
+     - Digital Radiography
+     - 112120
+     - ``x``
+     - [GAAL-2020]_
+     - 78484 (+11212)
+     - 22424
+
+.. warning:: **SHENZHEN/JSRT/CXR8 Dataset Support**
+
+  For some datasets (in which the annotations/masks are downloaded separately
+  from the dataset with the original images), both the original images and
+  annotations must be downloaded and placed inside the same directory, to match
+  the dataset reference dictionary's path.
+
+  * The Shenzhen_ root directory should then contain at least these two
+    subdirectories:
+
+    - ``CXR_png/`` (directory containing the CXR images)
+    - ``mask/`` (contains masks downloaded from `Shenzhen Annotations`_)
+
+  * The CXR8_ root directory:
+
+    - ``images/`` (directory containing the CXR images)
+    - ``segmentations/`` (contains masks downloaded from `CXR8 Annotations`_)
+
+  * The JSRT_ root directory:
+
+    - ``All247images/`` (directory containing the CXR images, in raw format)
+    - ``scratch/`` (contains masks downloaded from `JSRT Annotations`_)
+
+
 .. include:: links.rst

doc/links.rst

@@ -28,9 +28,111 @@
 .. _TBX11K: https://mmcheng.net/tb/
 .. _TBX11K_simplified: https://www.kaggle.com/datasets/vbookshelf/tbx11k-simplified
 
+.. _drive: https://github.com/wfdubowen/Retina-Unet/tree/master/DRIVE/
+.. _stare: http://cecas.clemson.edu/~ahoover/stare/
+.. _hrf: https://www5.cs.fau.de/research/data/fundus-images/
+.. _iostar: http://www.retinacheck.org/datasets
+.. _chase-db1: https://blogs.kingston.ac.uk/retinal/chasedb1/
+.. _drions-db: http://www.ia.uned.es/~ejcarmona/DRIONS-DB.html
+.. _rim-one r3: http://medimrg.webs.ull.es/research/downloads/
+.. _drishti-gs1: http://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/Home.php
+.. _refuge: https://refuge.grand-challenge.org/Details/
+.. _amd grand-challenge: https://amd.grand-challenge.org/
+.. _drhagis: https://personalpages.manchester.ac.uk/staff/niall.p.mcloughlin/
+.. _montgomery county: https://openi.nlm.nih.gov/faq#faq-tb-coll
+.. _jsrt: http://db.jsrt.or.jp/eng.php
+.. _jsrt-kaggle: https://www.kaggle.com/datasets/raddar/nodules-in-chest-xrays-jsrt
+.. _cxr8: https://nihcc.app.box.com/v/ChestXray-NIHCC
+
+.. Annotation data websites
+.. _shenzhen annotations: https://www.kaggle.com/yoctoman/shcxr-lung-mask
+.. _cxr8 annotations: https://github.com/lucasmansilla/NIH_chest_xray14_segmentations
+.. _jsrt annotations: https://www.isi.uu.nl/Research/Databases/SCR/download.php
+
 .. models
 .. _imagenet: https://www.image-net.org
 .. _alexnet: https://en.wikipedia.org/wiki/AlexNet
 .. _alexnet-pytorch: https://pytorch.org/hub/pytorch_vision_alexnet/
 .. _densenet: https://arxiv.org/abs/1608.06993
 .. _densenet-pytorch: https://pytorch.org/hub/pytorch_vision_densenet/
+
+.. Pretrained models
+
+.. _baselines_driu_drive: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/driu-drive-1947d9fa.pth
+.. _baselines_hed_drive: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/hed-drive-c8b86082.pth
+.. _baselines_m2unet_drive: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/m2unet-drive-ce4c7a53.pth
+.. _baselines_unet_drive: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/unet-drive-0ac99e2e.pth
+.. _baselines_driu_stare: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/driu-stare-79dec93a.pth
+.. _baselines_hed_stare: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/hed-stare-fcdb7671.pth
+.. _baselines_m2unet_stare: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/m2unet-stare-952778c2.pth
+.. _baselines_unet_stare: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/unet-stare-49b6a6d0.pth
+.. _baselines_driu_chase: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/driu-chasedb1-e7cf53c3.pth
+.. _baselines_hed_chase: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/hed-chasedb1-55ec6d34.pth
+.. _baselines_m2unet_chase: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/m2unet-chasedb1-0becbf29.pth
+.. _baselines_unet_chase: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/unet-chasedb1-be41b5a5.pth
+.. _baselines_driu_hrf: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/driu-hrf-c9e6a889.pth
+.. _baselines_hed_hrf: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/hed-hrf-3f4ab1c4.pth
+.. _baselines_m2unet_hrf: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/m2unet-hrf-2c3f2485.pth
+.. _baselines_unet_hrf: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/unet-hrf-9a559821.pth
+.. _baselines_driu_iostar: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/driu-iostar-vessel-ef8cc27b.pth
+.. _baselines_hed_iostar: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/hed-iostar-vessel-37cfaee1.pth
+.. _baselines_m2unet_iostar: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/m2unet-iostar-vessel-223b61ef.pth
+.. _baselines_unet_iostar: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/baselines/unet-iostar-vessel-86c78e87.pth
+
+.. _baselines_m2unet_jsrt: https://bobconda.lab.idiap.ch/public/data/bob/deepdraw/master/baselines/m2unet-jsrt-5f062009.pth
+.. _baselines_m2unet_montgomery: https://bobconda.lab.idiap.ch/public/data/bob/deepdraw/master/baselines/m2unet-montgomery-1c24519a.pth
+.. _baselines_m2unet_shenzhen: https://bobconda.lab.idiap.ch/public/data/bob/deepdraw/master/baselines/m2unet-shenzhen-7c9688e6.pth
+.. _baselines_lwnet_jsrt: https://bobconda.lab.idiap.ch/public/data/bob/deepdraw/master/baselines/lwnet-jsrt-73807eb1.pth
+.. _baselines_lwnet_montgomery: https://bobconda.lab.idiap.ch/public/data/bob/deepdraw/master/baselines/lwnet-montgomery-9c6bf39b.pth
+.. _baselines_lwnet_shenzhen: https://bobconda.lab.idiap.ch/public/data/bob/deepdraw/master/baselines/lwnet-shenzhen-10196d9c.pth
+
+.. _covd_driu_drive: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/driu/drive/model.pth
+.. _covd_hed_drive: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/hed/drive/model.pth
+.. _covd_m2unet_drive: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/m2unet/drive/model.pth
+.. _covd_unet_drive: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/unet/drive/model.pth
+.. _covd_driu_stare: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/driu/stare/model.pth
+.. _covd_hed_stare: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/hed/stare/model.pth
+.. _covd_m2unet_stare: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/m2unet/stare/model.pth
+.. _covd_unet_stare: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/unet/stare/model.pth
+.. _covd_driu_chase: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/driu/chasedb1/model.pth
+.. _covd_hed_chase: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/hed/chasedb1/model.pth
+.. _covd_m2unet_chase: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/m2unet/chasedb1/model.pth
+.. _covd_unet_chase: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/unet/chasedb1/model.pth
+.. _covd_driu_hrf: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/driu/hrf/model.pth
+.. _covd_hed_hrf: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/hed/hrf/model.pth
+.. _covd_m2unet_hrf: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/m2unet/hrf/model.pth
+.. _covd_unet_hrf: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/unet/hrf/model.pth
+.. _covd_driu_iostar: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/driu/iostar-vessel/model.pth
+.. _covd_hed_iostar: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/hed/iostar-vessel/model.pth
+.. _covd_m2unet_iostar: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/m2unet/iostar-vessel/model.pth
+.. _covd_unet_iostar: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/covd/unet/iostar-vessel/model.pth
+
+.. DRIVE
+.. _driu_drive.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/DRIU_DRIVE.pth
+.. _m2unet_drive.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_DRIVE.pth
+.. _m2unet_covd-drive.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-DRIVE.pth
+.. _m2unet_covd-drive_ssl.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-DRIVE_SSL.pth
+
+.. STARE
+.. _driu_stare.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/DRIU_STARE.pth
+.. _m2unet_stare.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_STARE.pth
+.. _m2unet_covd-stare.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-STARE.pth
+.. _m2unet_covd-stare_ssl.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-STARE_SSL.pth
+
+.. CHASE-DB1
+.. _driu_chasedb1.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/DRIU_CHASEDB1.pth
+.. _m2unet_chasedb1.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_CHASEDB1.pth
+.. _m2unet_covd-chasedb1.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-CHASEDB1.pth
+.. _m2unet_covd-chasedb1_ssl.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-CHASEDB1_SSL.pth
+
+.. IOSTAR
+.. _driu_iostar.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/DRIU_IOSTARVESSEL.pth
+.. _m2unet_iostar.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_IOSTARVESSEL.pth
+.. _m2unet_covd-iostar.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-IOSTAR.pth
+.. _m2unet_covd-iostar_ssl.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-IOSTAR_SSL.pth
+
+.. HRF
+.. _driu_hrf.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/DRIU_HRF1168.pth
+.. _m2unet_hrf.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_HRF1168.pth
+.. _m2unet_covd-hrf.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-HRF.pth
+.. _m2unet_covd-hrf_ssl.pth: https://www.idiap.ch/software/bob/data/bob/deepdraw/master/M2UNet_COVD-HRF_SSL.pth

doc/references.rst

@@ -106,3 +106,98 @@
    of precision, recall and F-score, with implication for evaluation**,
    European conference on Advances in Information Retrieval Research, 2005.
    https://doi.org/10.1007/978-3-540-31865-1_25
+
+.. [JSRT-2000] *J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T.
+   Kobayashi, K. Komatsu, M. Matsui, H. Fujita, Y. Kodera, K. Doi*,
+   **Development of a digital image database for chest radiographs with and
+   without a lung nodule: Receiver operating characteristic analysis of
+   radiologists’ detection of pulmonary nodules.**, American Journal of
+   Roentgenology. 2000. https://pubmed.ncbi.nlm.nih.gov/10628457
+
+.. [CXR8-2017] *Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu,
+   Mohammadhadi Bagheri, Ronald Summers*, **ChestX-ray8: Hospital-scale Chest
+   X-ray Database and Benchmarks on Weakly-Supervised Classification and
+   Localization of Common Thorax Diseases.**, IEEE CVPR, pp. 3462-3471, 2017.
+   https://arxiv.org/abs/1705.02315
+
+.. [GAAL-2020] *G. Gaál, B. Maga, A. Lukács*, **Attention U-Net Based
+   Adversarial Architectures for Chest X-ray Lung Segmentation.**, 2020.
+   https://arxiv.org/abs/2003.10304v1
+
+.. [DRISHTIGS1-2014] *J. Sivaswamy, S. R. Krishnadas, G. Datt Joshi, M. Jain and
+   A. U. Syed Tabish*, **Drishti-GS: Retinal image dataset for optic nerve
+   head (ONH) segmentation**, 2014 IEEE 11th International Symposium on
+   Biomedical Imaging (ISBI), Beijing, 2014, pp. 53-56.
+   https://doi.org/10.1109/ISBI.2014.6867807
+
+.. [DRIVE-2004] *J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever and B.
+   van Ginneken*, **Ridge-based vessel segmentation in color images of the
+   retina**, in IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp.
+   501-509, April 2004. https://doi.org/10.1109/TMI.2004.825627
+
+.. [ORLANDO-2017] *J. I. Orlando, E. Prokofyeva and M. B. Blaschko*, **A
+   Discriminatively Trained Fully Connected Conditional Random Field Model for
+   Blood Vessel Segmentation in Fundus Images**, in IEEE Transactions on
+   Biomedical Engineering, vol. 64, no. 1, pp. 16-27, Jan. 2017.
+   https://doi.org/10.1109/TBME.2016.2535311
+
+.. [MEYER-2017] *M. I. Meyer, P. Costa, A. Galdran, A. M. Mendonça, and A.
+   Campilho*, **A Deep Neural Network for Vessel Segmentation of Scanning Laser
+   Ophthalmoscopy Images**, in Image Analysis and Recognition, vol. 10317, F.
+   Karray, A. Campilho, and F. Cheriet, Eds. Cham: Springer International
+   Publishing, 2017, pp. 507–515. https://doi.org/10.1007/978-3-319-59876-5_56
+
+.. [REFUGE-2018] https://refuge.grand-challenge.org/Details/
+
+.. [CHASEDB1-2012] *M. M. Fraz et al.*, **An Ensemble Classification-Based
+   Approach Applied to Retinal Blood Vessel Segmentation**, in IEEE
+   Transactions on Biomedical Engineering, vol. 59, no. 9, pp. 2538-2548, Sept.
+   2012. https://doi.org/10.1109/TBME.2012.2205687
+
+.. [DRIONSDB-2008] *Enrique J. Carmona, Mariano Rincón, Julián García-Feijoó, José
+   M. Martínez-de-la-Casa*, **Identification of the optic nerve head with
+   genetic algorithms**, in Artificial Intelligence in Medicine, Volume 43,
+   Issue 3, pp. 243-259, 2008. http://dx.doi.org/10.1016/j.artmed.2008.04.005
+
+.. [HRF-2013] *A. Budai, R. Bock, A. Maier, J. Hornegger, and G. Michelson*,
+   **Robust Vessel Segmentation in Fundus Images**, in International Journal of
+   Biomedical Imaging, vol. 2013, p. 11, 2013.
+   http://dx.doi.org/10.1155/2013/154860
+
+.. [IOSTAR-2016] *J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits
+   and B. M. ter Haar Romeny*, **Robust Retinal Vessel Segmentation via Locally
+   Adaptive Derivative Frames in Orientation Scores**, in IEEE Transactions on
+   Medical Imaging, vol. 35, no. 12, pp. 2631-2644, Dec. 2016.
+
+.. [RIMONER3-2015] *F. Fumero, J. Sigut, S. Alayón, M. González-Hernández, M.
+   González de la Rosa*, **Interactive Tool and Database for Optic Disc and Cup
+   Segmentation of Stereo and Monocular Retinal Fundus Images**, Conference on
+   Computer Graphics, Visualization and Computer Vision, 2015.
+   https://dspace5.zcu.cz/bitstream/11025/29670/1/Fumero.pdf
+
+.. [SHENZHEN-2014] *S. Jaeger, S. Candemir, S. Antani, Y. X. Wáng, P. X. Lu, G.
+   Thoma*, **Two public chest X-ray datasets for computer-aided screening of
+   pulmonary diseases.**, Quantitative imaging in medicine and surgery. 2014.
+   https://doi:10.3978/j.issn.2223-4292.2014.11.20
+
+.. [STARE-2000] *A. D. Hoover, V. Kouznetsova and M. Goldbaum*, **Locating blood
+   vessels in retinal images by piecewise threshold probing of a matched filter
+   response**, in IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp.
+   203-210, March 2000. https://doi.org/10.1109/42.845178
+
+.. [SANDLER-2018] *M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C.h Chen*,
+   **MobileNetV2: Inverted Residuals and Linear Bottlenecks**, 2018.
+   https://arxiv.org/abs/1801.04381
+
+.. [RONNEBERGER-2015] *O. Ronneberger, P. Fischer, T. Brox*, **U-Net:
+   Convolutional Networks for Biomedical Image Segmentation**, 2015.
+   https://arxiv.org/abs/1505.04597
+
+.. [DRHAGIS-2017] *S. Holm, G. Russell, V. Nourrit, N. McLoughlin*, **DR HAGIS – A Novel Fundus Image Database for the Automatic Extraction of Retinal Surface Vessels**,
+   SPIE Journal of Medical Imaging, 2017.
+   https://doi.org/10.1117/1.jmi.4.1.014503
+
+.. [MC-2014] *S. Jaeger, S. Candemir, S. Antani, Y. X. Wáng, P. X. Lu, G.
+   Thoma*, **Two public chest X-ray datasets for computer-aided screening of
+   pulmonary diseases.**, Quantitative imaging in medicine and surgery. 2014.
+   https://doi:10.3978/j.issn.2223-4292.2014.11.20

src/mednet/libs/segmentation/config/data/cxr8/default.py

 # SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
-"""CXR8 dataset for Vessel Segmentation (default protocol).
+"""CXR8 Dataset (default protocol).
 
-* Split reference: [CXR8-2004]_
-* This configuration resolution: 544 x 544 (center-crop)
-* See :py:mod:`deepdraw.data.cxr8` for dataset details
-* This dataset offers a second-annotator comparison for the test set only
+* Split reference: [GAAL-2020]_
+* Configuration resolution: 256 x 256
 """
 
 from mednet.libs.segmentation.config.data.cxr8.datamodule import DataModule

src/mednet/libs/segmentation/config/data/drionsdb/expert1.py

@@ -5,7 +5,6 @@
 
 * Configuration resolution: 416 x 608 (after padding)
 * Split reference: [MANINIS-2016]_
-* See :py:mod:`deepdraw.data.drionsdb` for dataset details
 """
 
 from mednet.libs.segmentation.config.data.drionsdb.datamodule import DataModule

src/mednet/libs/segmentation/config/data/drionsdb/expert2.py

@@ -5,7 +5,6 @@
 
 * Configuration resolution: 416 x 608 (after padding)
 * Split reference: [MANINIS-2016]_
-* See :py:mod:`deepdraw.data.drionsdb` for dataset details
 """
 
 from mednet.libs.segmentation.config.data.drionsdb.datamodule import DataModule

src/mednet/libs/segmentation/config/data/drishtigs1/datamodule.py

@@ -105,12 +105,11 @@ class DataModule(CachingDataModule):
     and notching information.
 
     * Reference (including train/test split): [DRISHTIGS1-2014]_
-    * Original resolution (height x width): varying (min: 1749 x 2045, max: 1845 x
-    2468)
+    * Original resolution (height x width): varying (min: 1749 x 2045, max: 1845 x2468)
     * Configuration resolution: 1760 x 2048 (after center cropping)
     * Protocols ``optic-disc`` and ``optic-cup``:
-    * Training: 50
-    * Test: 51
+        * Training: 50
+        * Test: 51
 
     Parameters
     ----------

src/mednet/libs/segmentation/config/data/drishtigs1/optic_cup_all.py

@@ -5,7 +5,6 @@
 
 * Configuration resolution: 1760 x 2048 (after center cropping)
 * Reference (includes split): [DRISHTIGS1-2014]_
-* See :py:mod:`deepdraw.data.drishtigs1` for dataset details
 """
 
 from mednet.libs.segmentation.config.data.drishtigs1.datamodule import DataModule

src/mednet/libs/segmentation/config/data/drishtigs1/optic_cup_any.py

@@ -5,7 +5,6 @@
 
 * Configuration resolution: 1760 x 2048 (after center cropping)
 * Reference (includes split): [DRISHTIGS1-2014]_
-* See :py:mod:`deepdraw.data.drishtigs1` for dataset details
 """
 
 from mednet.libs.segmentation.config.data.drishtigs1.datamodule import DataModule

src/mednet/libs/segmentation/config/data/drishtigs1/optic_disc_all.py

@@ -5,7 +5,6 @@
 
 * Configuration resolution: 1760 x 2048 (after center cropping)
 * Reference (includes split): [DRISHTIGS1-2014]_
-* See :py:mod:`deepdraw.data.drishtigs1` for dataset details
 """
 
 from mednet.libs.segmentation.config.data.drishtigs1.datamodule import DataModule

src/mednet/libs/segmentation/config/data/drishtigs1/optic_disc_any.py

@@ -5,7 +5,6 @@
 
 * Configuration resolution: 1760 x 2048 (after center cropping)
 * Reference (includes split): [DRISHTIGS1-2014]_
-* See :py:mod:`deepdraw.data.drishtigs1` for dataset details
 """
 
 from mednet.libs.segmentation.config.data.drishtigs1.datamodule import DataModule

src/mednet/libs/segmentation/config/data/drive/default.py

@@ -5,7 +5,6 @@
 
 * Split reference: [DRIVE-2004]_
 * This configuration resolution: 544 x 544 (center-crop)
-* See :py:mod:`deepdraw.data.drive` for dataset details
 * This dataset offers a second-annotator comparison for the test set only
 """
 

src/mednet/libs/segmentation/config/data/drive/drive_2nd.py

@@ -5,7 +5,6 @@
 
 * Split reference: [DRIVE-2004]_
 * This configuration resolution: 544 x 544 (center-crop)
-* See :py:mod:`deepdraw.data.drive` for dataset details
 * This dataset offers a second-annotator comparison for the test set only
 """
 

src/mednet/libs/segmentation/config/data/hrf/default.py

@@ -5,7 +5,6 @@
 
 * Split reference: [ORLANDO-2017]_
 * Configuration resolution: 1168 x 1648 (about half full HRF resolution)
-* See :py:mod:`deepdraw.data.hrf` for dataset details
 """
 
 from mednet.libs.segmentation.config.data.hrf.datamodule import (

src/mednet/libs/segmentation/config/data/iostar/optic_disc.py

 # SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
+
+"""IOSTAR dataset for Optic Disc Segmentation (default protocol).
+
+* Split reference: [MEYER-2017]_
+* Configuration resolution: 1024 x 1024 (original resolution)
+"""
+
 from mednet.libs.segmentation.config.data.iostar.datamodule import DataModule
 
 datamodule = DataModule("optic-disc.json")

src/mednet/libs/segmentation/config/data/iostar/vessel.py

 # SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
+
+"""IOSTAR dataset for Vessel Segmentation (default protocol).
+
+* Split reference: [MEYER-2017]_
+* Configuration resolution: 1024 x 1024 (original resolution)
+"""
+
 from mednet.libs.segmentation.config.data.iostar.datamodule import DataModule
 
 datamodule = DataModule("vessel.json")

src/mednet/libs/segmentation/config/data/jsrt/datamodule.py

@@ -41,10 +41,10 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
     def load_pil_raw_12bit_jsrt(self, path: pathlib.Path) -> PIL.Image.Image:
         """Load a raw 16-bit sample data.
 
-        This method was designed to handle the raw images from the JSRT_ dataset.
+        This method was designed to handle the raw images from the JSRT dataset.
         It reads the data file and applies a simple histogram equalization to the
         8-bit representation of the image to obtain something along the lines of
-        the PNG (unofficial) version distributed at `JSRT-Kaggle`_.
+        the PNG (unofficial) version distributed at `JSRT-Kaggle`.
 
         Parameters
         ----------

src/mednet/libs/segmentation/config/data/jsrt/default.py

@@ -6,7 +6,6 @@
 
 * Split reference: [GAAL-2020]_
 * Configuration resolution: 256 x 256
-* See :py:mod:`deepdraw.data.jsrt` for dataset details
 """
 
 from mednet.libs.segmentation.config.data.jsrt.datamodule import (