# Deep Medical Toolkit (dmt)
This repo consists of a personal code toolkit for the purpose of facilitating medical imaging research. The main components of this library consists of a neural network model zoo, image transformations (for preprocessing & augmentation), common metrics/losses, fast multiprocessed data loading, and data structures for image samples.
## Implementation Details
### Similarities to [Torchio](https://github.com/fepegar/torchio)
- Same design hierarchy where samples (dict subclass) can hold arbitrary
attributes, and library-specific data like Images (e.g. ScalarImage3D),
Labels (e.g Masks)
- Transforms take samples (i.e. subjects) as they contain all the abstractions
and data format conversions built-in. Also their custom attributes feature
allows for easy storage of transformation history.
### Improvements Over [Torchio](https://github.com/fepegar/torchio)
- Overall objects and shift in design..
- Introduced data abstractions like samples (i.e. subject in torchio) and
examples (elements in a batch). This distinction is important.
- More extensible to allow custom behavior for data structures.
- Added general data structures for 2D & 3D images, and labels.
- 3D: ScalarMask3D, ScalarImage3D
- 2D: ScalarMask2D, ScalarImage2D, VectorImage2D
- Classification: CategoricalLabel
- Extended transformations to both 2D & 3D. Also added some 3D ones as well.
- Added 3D transforms:
- All 2D transforms:
- Improved existing data structures.
- For labels, added categorical (both multi-class & multi-label).
- For Images, gives you the option to permanently load data.
- Extensibility is improved for almost all data structures. For example, in
an Image, you can overload how a file is read, what preprocessing you want,
how to get an array/tensor from the preprocessed sitk image.
- Extended multiprocessing data loading for better flexibility, extensibility, and performance.
- Torchio has a Queue class that loades patches, DMT's equivalent of this is the PatchLoader class.
- This class continously loads patches rather than waiting for the queue to empty.
- DMT also has a DaemonLoader class that wraps the PyTorch DataLoader to continuously load samples.
- Added a model zoo that has both 2D & 3D neural networks.
- Added losses & metrics common to 2D & 3D computer vision tasks.
Additional Verbose Improvements
- Universally, numpy.ndarrays are passed around (instad of tensors like torchio)
- Sample images (one sample = one patient) are lazy-loaded as an sitk object
if a path is given.
### TODO:
General
- [x] Add weak references for Samples, Images, and Labels for easy access.
- [ ] Remove printing private attributes in __repr__ for images & others.
- [ ] For samples, and other relevant dict objects, check if reserved_attributes are not being overwritten.
Data
- [ ] Add mask + image overlap plotting for samples
- [ ] Add
Transforms
- [ ] Add image shape tracking to attributes for transforms (sample transform history).
- [ ] Add both 2D & 3D resized crop where you can set the scale.
