Update README.md

Author: taigw

README.md

@@ -1,5 +1,5 @@
 # PyMIC_examples
-[PyMIC][PyMIC_link] is an Pytorch-based medical image computing toolkit with deep learning. Here we provide a set of examples to show how it can be used for image classification and segmentation tasks. For beginners, you can follow the examples by just editting the configure files for model training, testing and evaluation. For advanced users, you can develop your own modules, such as customized networks and loss functions.  
+[PyMIC][PyMIC_link] is a PyTorch-based toolkit for medical image computing with annotation-efficient deep learning. Here we provide a set of examples to show how it can be used for image classification and segmentation tasks. For annotation efficient learning, we show examples of Semi-Supervised Learning (SSL), Weakly Supervised Learning (WSL) and Noisy Label Learning (NLL), respectively.  For beginners, you can follow the examples by just editting the configuration files for model training, testing and evaluation. For advanced users, you can easily develop your own modules, such as customized networks and loss functions.  
 
 ## Install PyMIC
 The latest released version of PyMIC can be installed by:
@@ -15,19 +15,19 @@ python setup.py install
 ``` 
 
 ## List of Examples
-Currently we provide two examples for image classification, and four examples for 2D/3D image segmentation. These examples include:
 
-1, [classification/AntBee][AntBee_link]: finetuning a resnet18 for Ant and Bee classification.
-
-2, [classification/CHNCXR][CHNCXR_link]: finetuning restnet18 and vgg16 for normal/tuberculosis X-ray image classification.
-
-3, [segmentation/JSRT][JSRT_link]: using a 2D UNet for heart segmentation from chest X-ray images.
-
-4, [segmentation/JSRT2][JSRT2_link]: defining a customized network for heart segmentation from chest X-ray images.
-
-5, [segmentation/fetal_hc][fetal_hc_link]: using a 2D UNet for fetal head segmentation from 2D ultrasound images.
-
-6, [segmentation/prostate][prostate_link]: using a 3D UNet for prostate segmentation from 3D MRI.
+Currently we provide the following examples in this repository:
+|Catetory|Example|Remarks|
+|---|---|---|
+|Classification|[AntBee][AntBee_link]|Finetuning a resnet18 for Ant and Bee classification|
+|Classification|[CHNCXR][CHNCXR_link]|Finetuning restnet18 and vgg16 for normal/tuberculosis X-ray image classification|
+|Fully supervised segmentation|[JSRT][JSRT_link]|Using a 2D UNet for lung segmentation from chest X-ray images|
+|Fully supervised segmentation|[JSRT2][JSRT2_link]|Using a customized network and loss function for the JSRT dataset|
+|Fully supervised segmentation|[Fetal_HC][fetal_hc_link]|Using a 2D UNet for fetal head segmentation from 2D ultrasound images|
+|Fully supervised segmentation|[Prostate][prostate_link]|Using a 3D UNet for prostate segmentation from 3D MRI|
+|Semi-supervised segmentation|[seg_ssl/ACDC][ssl_acdc_link]|Comparing different semi-supervised methods for heart structure segmentation|
+|Weakly-supervised segmentation|[seg_wsl/ACDC][wsl_acdc_link]|Segmentation of heart structure with scrible annotations|
+|Noisy label learning|[seg_nll/JSRT][nll_jsrt_link]|Comparing different NLL methods for learning from noisy labels|
 
 [PyMIC_link]: https://github.com/HiLab-git/PyMIC
 [AntBee_link]:classification/AntBee
@@ -36,4 +36,7 @@ Currently we provide two examples for image classification, and four examples fo
 [JSRT2_link]:segmentation/JSRT2
 [fetal_hc_link]:segmentation/fetal_hc
 [prostate_link]:segmentation/prostate
+[ssl_acdc_link]:seg_ssl/ACDC
+[wsl_acdc_link]:seg_wsl/ACDC 
+[nll_jsrt_link]:seg_nll/JSRT