Recent Releases of magms
magms - v2.2
API updates:
- Add support for adding not given modality as None
- Add support for additional arguments in nn.FeaturedData and nn.MAGNET2
- Introducing builder package
- Rename Mid*Fusion module to Mean*Fusion
- Skip loss calculation in losses.MAGLoss when a modality is not given
Other updates: - Minor bugs fixed - Typing improvement
- Python
Published by kisonho almost 2 years ago
magms - v2.2 (beta 3)
API updates:
- Add support for adding not given modality as None
- Add support for additional arguments in nn.FeaturedData and nn.MAGNET2
- Introducing builder package
- Rename Mid*Fusion module to Mean*Fusion
- Skip loss calculation in losses.MAGLoss when a modality is not given
Beta updates: - Typing improvement
- Python
Published by kisonho about 2 years ago
magms - v2.2 (Beta 2)
API updates:
- Add support for adding not given modality as None
- Add support for additional arguments in nn.FeaturedData and nn.MAGNET2
- Introducing builder package
- Rename Mid*Fusion module to Mean*Fusion
- Skip loss calculation in losses.MAGLoss when a modality is not given
Beta updates: - Minor bugs fixed
- Python
Published by kisonho over 2 years ago
magms - v2.2 (Beta 1)
API updates:
- Add support for adding not given modality as None
- Add support for additional arguments in nn.FeaturedData and nn.MAGNET2
- Introducing builder package
- Rename Mid*Fusion module to Mean*Fusion
- Skip loss calculation in losses.MAGLoss when a modality is not given
Other updates: - Minor bugs fixed - Typing improvement
- Python
Published by kisonho over 2 years ago
magms - v2.1
API Updates:
- Implement torchmanager_monai.Manager as general manager for monai
- Introduced losses.MAGMSLoss
- Missing modalities training support added during unpacking data in MonaiManager
- Monai dependency is now optional with torchmanager_monai as its extra package (MonaiManager, networks.UNETR, and networks.UNETRWithMultiModality will be defined as NotImplemented)
- Rename magnet.managers.monai.Manager to magnet.manager.monai.SegmentationManager
- Rename torchmanager_monai.MonaiManager to SegmentationManager which inherits torchmanager_monai.Monai
Other updates:
- Deprecated HeMIS implementation
- Minor bugs fixed
- Remove losses.protocols.DistillatedData
- Python
Published by kisonho over 2 years ago
magms - v2.1 (Beta 2)
API Updates:
- Implement torchmanager_monai.Manager as general manager for monai
- Introduced losses.MAGMSLoss
- Monai dependency is now optional with torchmanager_monai as its extra package (MonaiManager, networks.UNETR, and networks.UNETRWithMultiModality will be defined as NotImplemented)
- Rename magnet.managers.monai.Manager to magnet.manager.monai.SegmentationManager
- Rename torchmanager_monai.MonaiManager to SegmentationManager which inherits torchmanager_monai.Monai
Other updates:
- Deprecated HeMIS implementation
- Minor bugs fixed
- Remove losses.protocols.DistillatedData
- Python
Published by kisonho over 2 years ago
magms - v2.1 (Beta 1)
API Updates:
- Implement torchmanager_monai.Manager as general manager for monai
- Introduced losses.MAGMSLoss
- Monai dependency is now optional with torchmanager_monai as its extra package (MonaiManager, networks.UNETR, and networks.UNETRWithMultiModality will be defined as NotImplemented)
- Rename magnet.managers.monai.Manager to magnet.manager.monai.SegmentationManager
- Rename torchmanager_monai.MonaiManager to SegmentationManager which inherits torchmanager_monai.Monai
Other updates:
- Minor bugs fixed
- Missing modalities training support added during unpacking data in MonaiManager
- Python
Published by kisonho over 2 years ago
magms - v2.0
Feature updates:
- Add self distillation losses for MAG-MS framework (losses.MAGFeatureDistillationLoss and losses.MAGSelfDistillationLoss)
- Add a copy_encoder boolean flag to control if use the same initialized weights for modality specific encoders
- Introducing MAGNET2 architecture with feature fusion
- Handling available targets options in HeMIS when target_dict does not give all available modalities but inputs contain all the modalities
- Python framework renamed to magms
- The target property can now be set as a list of target modalities in both TargetingManager and MAGNET
- Python
Published by kisonho over 2 years ago
magms - v1.1
API updates:
- A traditional torch.data.Dataset and torch.data.DataLoader can now be used to train with all modalities inputs instead of using magnet.data.TargetingDataset and magnet.data.TargetingDataLoader
- Add a copy_modality boolean flag control for build method
- Add HeMIS network implementation as a comparison
- Deprecate UNETRWithDictOutput for dictionary outputs
- Introducing magnet.losses packages where magnet.losses.MAGLoss can be used to combine the training of Magnet in one iteration
- Introducing networks.unetr package
- Targeting protocol now accepts optional keys in target_dict property for all modalities
- The target property can now be set as a list of target modalities in both TargetingManager and MAGNET
- Python
Published by kisonho almost 3 years ago