Merge branch 'main' of https://github.com/HiLab-git/PyMIC_examples into main

Author: taigw

README.md

@@ -1,8 +1,6 @@
 # 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_link]: https://github.com/HiLab-git/PyMIC
- 
 ## Install PyMIC
 To start, you can install the latest released version of PyMIC by:
 
@@ -15,17 +13,23 @@ To use the latest development version, you can download the source code [here][P
 ## 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`: finetuning a resnet18 for Ant and Bee classification.
-
-2, `classification/CHNCXR`: finetuning restnet18 and vgg16 for normal/tuberculosis X-ray image classification.
+1, [classification/AntBee][AntBee_link]: finetuning a resnet18 for Ant and Bee classification.
 
-3, `segmentation/JSRT`: using a 2D UNet for heart segmentation from chest X-ray images.
+2, [classification/CHNCXR][CHNCXR_link]: finetuning restnet18 and vgg16 for normal/tuberculosis X-ray image classification.
 
-4, `segmentation/JSRT2`: defining a customized network for heart segmentation from chest X-ray images.
+3, [segmentation/JSRT][JSRT_link]: using a 2D UNet for heart segmentation from chest X-ray images.
 
-5, `segmentation/fetal_hc`: using a 2D UNet for fetal head segmentation from 2D ultrasound images.
+4, [segmentation/JSRT2][JSRT2_link]: defining a customized network for heart segmentation from chest X-ray images.
 
-6, `segmentation/prostate`: using a 3D UNet for prostate segmentation from 3D MRI.
+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.
 
+[PyMIC_link]: https://github.com/HiLab-git/PyMIC
+[AntBee_link]:classification/AntBee
+[CHNCXR_link]:classification/CHNCXR
+[JSRT_link]:segmentation/JSRT
+[JSRT2_link]:segmentation/JSRT2
+[fetal_hc_link]:segmentation/fetal_hc
+[prostate_link]:segmentation/prostate