docs: add links and fix example

aucmedi/__init__.py

@@ -24,10 +24,10 @@
 Build your state-of-the-art medical image classification pipeline with the 3 AUCMEDI pillars:
 
 !!! info "Pillars of AUCMEDI"
-    | Pillar                                                                       | Description                                                    |
+    | Pillar                                                                    | Description                                                       |
     | ------------------------------------------------------------------------- | ----------------------------------------------------------------- |
     | #1: [input_interface()][aucmedi.data_processing.io_data.input_interface]  | Obtaining general information from the dataset.                   |
-    | #2: [NeuralNetwork][aucmedi.neural_network.model.NeuralNetwork]         | Building the deep learning model.                                 |
+    | #2: [NeuralNetwork][aucmedi.neural_network.model.NeuralNetwork]           | Building the deep learning model.                                 |
     | #3: [DataGenerator][aucmedi.data_processing.data_generator.DataGenerator] | Powerful interface for loading any images/volumes into the model. |
 
 

aucmedi/neural_network/architectures/classifier.py

@@ -67,7 +67,7 @@ class Classifier:
         | Multi-label classification | `activation_output="sigmoid"`: Multiple classes can be correct. |
 
         For more information on multi-class vs multi-label, check out this blog post from Rachel Draelos: <br>
-        https://glassboxmedicine.com/2019/05/26/classification-sigmoid-vs-softmax/
+        [https://glassboxmedicine.com/2019/05/26/classification-sigmoid-vs-softmax/](https://glassboxmedicine.com/2019/05/26/classification-sigmoid-vs-softmax/)
 
     The recommended way is to pass all required variables to the [NeuralNetwork][aucmedi.neural_network.model.NeuralNetwork]
     which automatically creates the Classifier and passes it to the Architecture.
@@ -134,7 +134,7 @@ def __init__(self, n_labels, activation_output="softmax",
     def build(self, model_input, model_output):
         """ Internal function which appends the classification head.
 
-        This function will be called inside of an [Architecture][aucmedi.neural_network.architectures] `create_model()` function
+        This function will be called from inside an [Architecture][aucmedi.neural_network.architectures] `create_model()` function
         and must return a functional Keras model.
         The `build()` function will append a classification head to the provided Keras model.
 

aucmedi/neural_network/architectures/image/densenet121.py

@@ -28,13 +28,13 @@
 | Standardization          | "torch"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/applications/#densenet <br>
+    [https://keras.io/applications/#densenet](https://keras.io/applications/#densenet) <br>
 
 ???+ abstract "Reference - Publication"
     Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger. 25 Aug 2016.
     Densely Connected Convolutional Networks.
     <br>
-    https://arxiv.org/abs/1608.06993
+    [https://arxiv.org/abs/1608.06993](https://arxiv.org/abs/1608.06993)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/densenet169.py

@@ -28,13 +28,13 @@
 | Standardization          | "torch"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/applications/#densenet <br>
+    [https://keras.io/applications/#densenet](https://keras.io/applications/#densenet) <br>
 
 ???+ abstract "Reference - Publication"
     Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger. 25 Aug 2016.
     Densely Connected Convolutional Networks.
     <br>
-    https://arxiv.org/abs/1608.06993
+    [https://arxiv.org/abs/1608.06993](https://arxiv.org/abs/1608.06993)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/densenet201.py

@@ -28,13 +28,13 @@
 | Standardization          | "torch"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/applications/#densenet <br>
+    [https://keras.io/applications/#densenet](https://keras.io/applications/#densenet) <br>
 
 ???+ abstract "Reference - Publication"
     Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger. 25 Aug 2016.
     Densely Connected Convolutional Networks.
     <br>
-    https://arxiv.org/abs/1608.06993
+    [https://arxiv.org/abs/1608.06993](https://arxiv.org/abs/1608.06993)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/efficientnetb0.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/efficientnet/ <br>
+    [https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Mingxing Tan, Quoc V. Le. 28 May 2019.
     EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
     <br>
-    https://arxiv.org/abs/1905.11946
+    [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/efficientnetb1.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/efficientnet/ <br>
+    [https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Mingxing Tan, Quoc V. Le. 28 May 2019.
     EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
     <br>
-    https://arxiv.org/abs/1905.11946
+    [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/efficientnetb2.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/efficientnet/ <br>
+    [https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Mingxing Tan, Quoc V. Le. 28 May 2019.
     EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
     <br>
-    https://arxiv.org/abs/1905.11946
+    [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/efficientnetb3.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/efficientnet/ <br>
+    [https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Mingxing Tan, Quoc V. Le. 28 May 2019.
     EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
     <br>
-    https://arxiv.org/abs/1905.11946
+    [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/efficientnetb4.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/efficientnet/ <br>
+    [https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Mingxing Tan, Quoc V. Le. 28 May 2019.
     EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
     <br>
-    https://arxiv.org/abs/1905.11946
+    [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/efficientnetb5.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/efficientnet/ <br>
+    [https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Mingxing Tan, Quoc V. Le. 28 May 2019.
     EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
     <br>
-    https://arxiv.org/abs/1905.11946
+    [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/efficientnetb6.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/efficientnet/ <br>
+    [https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Mingxing Tan, Quoc V. Le. 28 May 2019.
     EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
     <br>
-    https://arxiv.org/abs/1905.11946
+    [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/efficientnetb7.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/efficientnet/ <br>
+    [https://keras.io/api/applications/efficientnet/](https://keras.io/api/applications/efficientnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Mingxing Tan, Quoc V. Le. 28 May 2019.
     EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
     <br>
-    https://arxiv.org/abs/1905.11946
+    [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/inceptionresnetv2.py

@@ -28,13 +28,13 @@
 | Standardization          | "tf"                       |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/inceptionresnetv2/ <br>
+    [https://keras.io/api/applications/inceptionresnetv2/](https://keras.io/api/applications/inceptionresnetv2/) <br>
 
 ???+ abstract "Reference - Publication"
     Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi. 23 Feb 2016.
     Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.
     <br>
-    https://arxiv.org/abs/1602.07261
+    [https://arxiv.org/abs/1602.07261](https://arxiv.org/abs/1602.07261)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/inceptionv3.py

@@ -28,13 +28,13 @@
 | Standardization          | "tf"                       |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/inceptionv3/ <br>
+    [https://keras.io/api/applications/inceptionv3/](https://keras.io/api/applications/inceptionv3/) <br>
 
 ???+ abstract "Reference - Publication"
     Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna. 2 Dec 2015.
     Rethinking the Inception Architecture for Computer Vision.
     <br>
-    https://arxiv.org/abs/1512.00567
+    [https://arxiv.org/abs/1512.00567](https://arxiv.org/abs/1512.00567)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/mobilenet.py

@@ -28,14 +28,14 @@
 | Standardization          | "tf"                       |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/mobilenet/ <br>
+    [https://keras.io/api/applications/mobilenet/](https://keras.io/api/applications/mobilenet/) <br>
 
 ???+ abstract "Reference - Publication"
     Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand,
     Marco Andreetto, Hartwig Adam. 17 Apr 2017.
     MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.
     <br>
-    https://arxiv.org/abs/1704.04861
+    [https://arxiv.org/abs/1704.04861](https://arxiv.org/abs/1704.04861)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/mobilenetv2.py

@@ -28,13 +28,13 @@
 | Standardization          | "tf"                       |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/mobilenet/ <br>
+    [https://keras.io/api/applications/mobilenet/](https://keras.io/api/applications/mobilenet/) <br>
 
 ???+ abstract "Reference - Publication"
     Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. 13 Jan 2018.
     MobileNetV2: Inverted Residuals and Linear Bottlenecks.
     <br>
-    https://arxiv.org/abs/1801.04381
+    [https://arxiv.org/abs/1801.04381](https://arxiv.org/abs/1801.04381)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/nasnetlarge.py

@@ -28,13 +28,13 @@
 | Standardization          | "tf"                       |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/nasnet/ <br>
+    [https://keras.io/api/applications/nasnet/](https://keras.io/api/applications/nasnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. 10 Dec 2015.
     Learning Transferable Architectures for Scalable Image Recognition.
     <br>
-    https://arxiv.org/abs/1707.07012
+    [https://arxiv.org/abs/1707.07012](https://arxiv.org/abs/1707.07012)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/nasnetmobile.py

@@ -28,13 +28,13 @@
 | Standardization          | "tf"                       |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/nasnet/ <br>
+    [https://keras.io/api/applications/nasnet/](https://keras.io/api/applications/nasnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. 10 Dec 2015.
     Learning Transferable Architectures for Scalable Image Recognition.
     <br>
-    https://arxiv.org/abs/1707.07012
+    [https://arxiv.org/abs/1707.07012](https://arxiv.org/abs/1707.07012)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/resnet101.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/resnet/ <br>
+    [https://keras.io/api/applications/resnet/](https://keras.io/api/applications/resnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 10 Dec 2015.
     Deep Residual Learning for Image Recognition.
     <br>
-    https://arxiv.org/abs/1512.03385
+    [https://arxiv.org/abs/1512.03385](https://arxiv.org/abs/1512.03385)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/resnet101v2.py

@@ -28,13 +28,13 @@
 | Standardization          | "tf"                       |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/resnet/ <br>
+    [https://keras.io/api/applications/resnet/](https://keras.io/api/applications/resnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 16 Mar 2016.
     Identity Mappings in Deep Residual Networks.
     <br>
-    https://arxiv.org/abs/1603.05027
+    [https://arxiv.org/abs/1603.05027](https://arxiv.org/abs/1603.05027)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/resnet152.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/resnet/ <br>
+    [https://keras.io/api/applications/resnet/](https://keras.io/api/applications/resnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 10 Dec 2015.
     Deep Residual Learning for Image Recognition.
     <br>
-    https://arxiv.org/abs/1512.03385
+    [https://arxiv.org/abs/1512.03385](https://arxiv.org/abs/1512.03385)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/resnet152v2.py

@@ -28,13 +28,13 @@
 | Standardization          | "tf"                       |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/resnet/ <br>
+    [https://keras.io/api/applications/resnet/](https://keras.io/api/applications/resnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 16 Mar 2016.
     Identity Mappings in Deep Residual Networks.
     <br>
-    https://arxiv.org/abs/1603.05027
+    [https://arxiv.org/abs/1603.05027](https://arxiv.org/abs/1603.05027)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/resnet50.py

@@ -28,13 +28,13 @@
 | Standardization          | "caffe"                    |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/resnet/ <br>
+    [https://keras.io/api/applications/resnet/](https://arxiv.org/abs/1707.07012) <br>
 
 ???+ abstract "Reference - Publication"
     Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 10 Dec 2015.
     Deep Residual Learning for Image Recognition.
     <br>
-    https://arxiv.org/abs/1512.03385
+    [https://arxiv.org/abs/1512.03385](https://arxiv.org/abs/1512.03385)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #

aucmedi/neural_network/architectures/image/resnet50v2.py

@@ -28,13 +28,13 @@
 | Standardization          | "tf"                       |
 
 ???+ abstract "Reference - Implementation"
-    https://keras.io/api/applications/resnet/ <br>
+    [https://keras.io/api/applications/resnet/](https://keras.io/api/applications/resnet/) <br>
 
 ???+ abstract "Reference - Publication"
     Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 16 Mar 2016.
     Identity Mappings in Deep Residual Networks.
     <br>
-    https://arxiv.org/abs/1603.05027
+    [https://arxiv.org/abs/1603.05027](https://arxiv.org/abs/1603.05027)
 """
 #-----------------------------------------------------#
 #                   Library imports                   #