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Monai version has been updated to 0.9. See what new functions it has
2022-07-07 10:37:00 【Sister Tina】
MONAI Update to 0.9 Version of the , How much do you use ? Let's take a look at the important updates this time .
- MONAI Bundle:MONAI Bundles
- Object detection in medical images: Object detection in medical images
- Swin Transformers for 3D medical image analysis: be used for 3D Medical image analysis Swin Transformers
- New interactive segmentation components: New interactive subdivision components
- MetaTensor API preview:MetaTensor API preview
MONAI Bundle
MONAI Bundle The format defines a deep learning model of portable description ( file ). The bundle includes the key information necessary in the model development lifecycle , And allow users and programs to understand the purpose and purpose of the model . Bundle and monai.bundle API The main advantage of :
- Standardized packaging format for storing and sharing models,
A standardized packaging format for storing and sharing models - Structured configuration files for fast prototyping of deep learning workflows,
Structured configuration file for deep learning workflow rapid prototyping , - Easy to program APIs to separate deep learning hyperparameter settings from the Python code,
Easy to program API To set the depth learning superparameter with Python Code separation , - Flexible config components to allow for different low-level Python implementations,
Flexible configuration of components to allow different low-level Python Realization , - Help to decouple the component details from higher level learning paradigms such as federated learning and AutoML.
Help integrate component details with higher-level learning paradigms ( For example, federal learning and AutoML) decoupling 、
This part is suitable for technical leaders who have high requirements for code writing , I don't understand this kind of Xiaobai . More detailed tutorials MONAI bundle
Object detection in medical images
This release includes the basic components of the object localization and classification workflow . The initial development included 2D and 3D Bounding box processing 、RetinaNet Network block and architecture , And coordinate based preprocessing 、 Hard negative sampler and other commonly used practical modules .
Those who do object checking can pay attention to this part of the update .
Swin Transformers for 3D medical image analysis
Added Swin UNETR Model in MONAI To realize . This tutorial shows an example of multi organ segmentation using this state-of-the-art model , Among them, the weight comes from 5050 Time CT Scanned Swin UNETR Encoder (3D Swin Transformer) Self supervision pre training .
Implementation details in MOANI swin UNETR
I've been paying attention recently transformer Split children's shoes can try to run the code .
New interactive segmentation components
DeepEdit and NuClick The new components in the interactive segmentation workflow of equal depth learning have been integrated into the core code base . They are MONAILabel Basic building blocks for the latest functionality in .
Useful MONAI label Understand the marked data , I haven't understood how to use
MetaTensor API preview
Metadata related to major imaging patterns is important in many biomedical applications , Especially for MONAI The data-driven method that has been concerned . From this version , We are right. MONAI The data representation in has been significantly reconstructed . First step , Core data structure MetaTensor and MetaObj Implemented as function Preview . Further development of the functional branches will be provided in future milestone releases .
Interested in this update , It is recommended to upgrade and play ,PS: If you publish a paper with a previous version , Still under repair , It is suggested to build a new conda Environmental Science , Avoiding environmental changes cannot reproduce the experiment .
Articles are constantly updated , You can pay attention to the official account of WeChat 【 Medical image AI combat camp 】 Get the latest , The official account of the frontier technology in the field of medical image processing . Stick to the practice , Take you hand in hand to do the project , Play the game , Write a paper . All original articles provide theoretical explanation , Experimental code , experimental data . Only practice can grow faster , Pay attention to our , Learn together ~
I am a Tina, I'll see you on our next blog ~
Working during the day and writing at night , cough
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