cytounet 0.2.2

Changes to cytounet

Version 0.2.2

• Fixed a bug in setting test paths when using script mode.

• Versioning is now automated, as is linking to the GitHub release. Please ensure you release in the
  form v#versionnumberhere.

• Fixed issues with script mode

• Using tensorflow.Keras instead of Keras.

• Added sanity checks to ensure paths actually exist.

Version 0.2.1

• Extended script to handle fine-tuning and from scratch-training

• Added a script only mode.

• Added support for docs.

• Added original a549 sample data, notebook, and pre-trained weights.

• Added experimental results to the README.

• Fixed issues with original images being overwritten. It is now possible to return a copy of non
  overwritten images.

• Made draw_contours more flexible. Specifically, it is now possible to turn off text display as
  this makes the image crowded.

• Added find_contours and draw_contours, useful methods for area determination.

• Added read_image_spec for use only for post modeling processing. This fixes issues with incorrect
  shapes when using read_images

Version 0.2.0

• Kernel regularization can now be turned off via a boolean argument(use_regularizer)

• Added a new data set from BBBC.

• finetune is a new function dedicated to the finetuning workflow.

• Regularization is now supported. It is currently limited to L1 and L2.

• pretrained_weights was dropped as an argument to unet. Use a callback instead. A future
  version wil include a fine tuning function.

• save_as was removed from train. Use ModelCheckpoint instead and provide it as a callback.

• show_images now shows titles. These functions will be removed later and imported from pyautocv
  instead.

• Fixed issues with reading mixed jpg and png images.

• Added reshape_images and resize_images. These are helper functions that may be useful when plotting
  or restoring original image size.

• show_images and read_images are now imported from pyautocv >= 0.2.2

• Fixed issues with inconsistent image order in show_images when reading from a directory.

• Added filename printing to data generators to make it easier to show what order the files are
  being read in. This can be disabled by setting show_names to False.

• Changes to prediction generation were made. We now use ImageDataGenerator for
  test time data generation.

• Fixed a bug related to load_augmentations that led to image flipping.

• Changed outputs to sigmoid instead of ReLU

• Updated to latest API ie predict vs predict_generator

• Added train to simplify model fitting.

• Added predict to reduce code repetition and make predicting easier.

• unet was rewritten to increase complexity and solve issues with blank predictions. It now also uses Conv2DTranspose instead of UpSampling2D.

• Initial support for a simpler model to optimise the bias-variance trade off for small(er) datasets.

• Removed Dropout since this is known to have no improvement over Batch Normalisation.

• Initial support for SGD as the default optimiser

• Moved from camelCase to snake_case, now using more descriptive function names.

• Fixed issues with list input to show_images

---

• Release 0.1.0

---

• Renamed repository to cytounet to reflect the heavy focus on biological images.

• Initiated support for validation via validGenerator.

• Fixed issues with show_images failing to load numpy ndarray images.

---

• Initiated ability to install with pip and setup.py.

---

• show_augmented was renamed to show_images and refactored as a more general method not limited

to just augmented images. A cmap argument was also added for more flexibility. This replaces labelVisualize
which has now been dropped.

• Introduced a separate save method for images and predictions. Use saveImages and savePredictions
  respectively.

---

• Fixed issues with information loss following saving of predictions.

• geneTrainNpY was refactored and renamed LoadAugmented

• Added thresholdImages to threshold masks(mostly). Please see pyautocv
  for a more general and flexible way to manipulate images.

• Added saveImages, a helper to save images as(by default) .tif. This is because biological
  images are normally tiff in nature.

• Removed savePredictions. Use saveImages instead.

---

• Updated module documentation

• adjustData was removed since it had known issues. It may be restored in the future.

• Fixed issues that resulted in blank predictions

• Added show_augmented to show results of data augmentation

• Added BatchNormarmalisation steps

• Training made more flexible by allowing usage of different metrics and loss functions without editing source code(i.e change on the fly)

• Saving and image reading functions made more flexible to read/save any image file format.

• Made most functions compatible with Keras >= 2.0

• Added dice loss and dice coefficient.

cytounet: Release Notes

Changes to cytounet

Version 0.2.1

• Extended script to handle fine-tuning and from scratch-training

• Added a script only mode.

• Added support for docs.

• Added original a549 sample data, notebook, and pre-trained weights.

• Added experimental results to the README.

• Fixed issues with original images being overwritten. It is now possible to return a copy of non
  overwritten images.

• Made draw_contours more flexible. Specifically, it is now possible to turn off text display as
  this makes the image crowded.

• Added find_contours and draw_contours, useful methods for area determination.

• Added read_image_spec for use only for post modeling processing. This fixes issues with incorrect
  shapes when using read_images

Version 0.2.0

• Kernel regularization can now be turned off via a boolean argument(use_regularizer)

• Added a new data set from BBBC.

• finetune is a new function dedicated to the finetuning workflow.

• Regularization is now supported. It is currently limited to L1 and L2.

• pretrained_weights was dropped as an argument to unet. Use a callback instead. A future
  version wil include a fine tuning function.

• save_as was removed from train. Use ModelCheckpoint instead and provide it as a callback.

• show_images now shows titles. These functions will be removed later and imported from pyautocv
  instead.

• Fixed issues with reading mixed jpg and png images.

• Added reshape_images and resize_images. These are helper functions that may be useful when plotting
  or restoring original image size.

• show_images and read_images are now imported from pyautocv >= 0.2.2

• Fixed issues with inconsistent image order in show_images when reading from a directory.

• Added filename printing to data generators to make it easier to show what order the files are
  being read in. This can be disabled by setting show_names to False.

• Changes to prediction generation were made. We now use ImageDataGenerator for
  test time data generation.

• Fixed a bug related to load_augmentations that led to image flipping.

• Changed outputs to sigmoid instead of ReLU

• Updated to latest API ie predict vs predict_generator

• Added train to simplify model fitting.

• Added predict to reduce code repetition and make predicting easier.

• unet was rewritten to increase complexity and solve issues with blank predictions. It now also uses Conv2DTranspose instead of UpSampling2D.

• Initial support for a simpler model to optimise the bias-variance trade off for small(er) datasets.

• Removed Dropout since this is known to have no improvement over Batch Normalisation.

• Initial support for SGD as the default optimiser

• Moved from camelCase to snake_case, now using more descriptive function names.

• Fixed issues with list input to show_images

---

• Release 0.1.0

---

• Renamed repository to cytounet to reflect the heavy focus on biological images.

• Initiated support for validation via validGenerator.

• Fixed issues with show_images failing to load numpy ndarray images.

---

• Initiated ability to install with pip and setup.py.

---

• show_augmented was renamed to show_images and refactored as a more general method not limited

to just augmented images. A cmap argument was also added for more flexibility. This replaces labelVisualize
which has now been dropped.

• Introduced a separate save method for images and predictions. Use saveImages and savePredictions
  respectively.

---

• Fixed issues with information loss following saving of predictions.

• geneTrainNpY was refactored and renamed LoadAugmented

• Added thresholdImages to threshold masks(mostly). Please see pyautocv
  for a more general and flexible way to manipulate images.

• Added saveImages, a helper to save images as(by default) .tif. This is because biological
  images are normally tiff in nature.

• Removed savePredictions. Use saveImages instead.

---

• Updated module documentation

• adjustData was removed since it had known issues. It may be restored in the future.

• Fixed issues that resulted in blank predictions

• Added show_augmented to show results of data augmentation

• Added BatchNormarmalisation steps

• Training made more flexible by allowing usage of different metrics and loss functions without editing source code(i.e change on the fly)

• Saving and image reading functions made more flexible to read/save any image file format.

• Made most functions compatible with Keras >= 2.0

• Added dice loss and dice coefficient.

cytounet: A Deep Learning based Cell Segmentation Library

This version marks the first production ready version of cytounet. It includes several improvements from the previous version, listed in the release notes below.

Changes to cytounet

Version 0.2.0

• Kernel regularization can now be turned off via a boolean argument(use_regularizer)

• Added a new data set from BBBC.

• finetune is a new function dedicated to the finetuning workflow.

• Regularization is now supported. It is currently limited to L1 and L2.

• pretrained_weights was dropped as an argument to unet. Use a callback instead. A future
  version wil include a fine tuning function.

• save_as was removed from train. Use ModelCheckpoint instead and provide it as a callback.

• show_images now shows titles. These functions will be removed later and imported from pyautocv
  instead.

• Fixed issues with reading mixed jpg and png images.

• Added reshape_images and resize_images. These are helper functions that may be useful when plotting
  or restoring original image size.

• show_images and read_images are now imported from pyautocv >= 0.2.2

• Fixed issues with inconsistent image order in show_images when reading from a directory.

• Added filename printing to data generators to make it easier to show what order the files are
  being read in. This can be disabled by setting show_names to False.

• Changes to prediction generation were made. We now use ImageDataGenerator for
  test time data generation.

• Fixed a bug related to load_augmentations that led to image flipping.

• Changed outputs to sigmoid instead of ReLU

• Updated to latest API ie predict vs predict_generator

• Added train to simplify model fitting.

• Added predict to reduce code repetition and make predicting easier.

• unet was rewritten to increase complexity and solve issues with blank predictions. It now also uses Conv2DTranspose instead of UpSampling2D.

• Initial support for a simpler model to optimise the bias-variance trade off for small(er) datasets.

• Removed Dropout since this is known to have no improvement over Batch Normalisation.

• Initial support for SGD as the default optimiser

• Moved from camelCase to snake_case, now using more descriptive function names.

• Fixed issues with list input to show_images

Introducing cytounet, a Keras-Tensorflow based Unet

cytounet's changelog.

• Release 0.1.0

---

• Renamed repository to cytounet to reflect the havy focus on biological images.

• Initiated support for validation via validGenerator.

• Fixed issues with show_images failing to load numpy ndarray images.

---

• Initiated ability to install with pip and setup.py.

---

• show_augmented was renamed to show_images and refactored as a more general method not limited

to just augmented images. A cmap argument was also added for more flexibility. This replaces labelVisualize
which has now been dropped.

• Introduced a separate save method for images and predictions. Use saveImages and savePredictions
  respectively.

---

• Fixed issues with information loss following saving of predictions.

• geneTrainNpY was refactored and renamed LoadAugmented

• Added thresholdImages to threshold masks(mostly). Please see pyautocv
  for a more general and flexible way to manipulate images.

• Added saveImages, a helper to save images as(by default) .tif. This is because biological
  images are normally tiff in nature.

• Removed savePredictions. Use saveImages instead.

---

• Updated module documentation

• adjustData was removed since it had known issues. It may be restored in the future.

• Fixed issues that resulted in blank predictions

• Added show_augmented to show results of data augmentation

• Added BatchNormarmalisation steps

• Training made more flexible by allowing usage of different metrics and loss functions without editing source code(i.e change on the fly)

• Saving and image reading functions made more flexible to read/save any image file format.

• Made most functions compatible with Keras >= 2.0

• Added dice loss and dice coefficient.