BiomedSciAI · egozi · Mar 15, 2022 · Mar 15, 2022 · Mar 20, 2022 · Mar 25, 2022
diff --git a/.gitignore b/.gitignore
@@ -29,10 +29,10 @@ lib64
 **/*.log
 Result
 __pycache__
-fuse_examples/classification/knight/baseline/*.csv
-fuse_examples/classification/knight/baseline/clinical_data/*
-fuse_examples/classification/knight/baseline/model_dir
+fuse_examples/imaging/classification/knight/baseline/*.csv
+fuse_examples/imaging/classification/knight/baseline/clinical_data/*
+fuse_examples/imaging/classification/knight/baseline/model_dir
 .gitignore.save
-fuse_examples/classification/mnist/examples
-fuse_examples/tutorials/hello_world/examples/
+fuse_examples/imaging/classification/mnist/examples
+fuse_examples/imaging/hello_world/examples/
 .vscode/
diff --git a/README.md b/README.md
@@ -51,31 +51,35 @@ $ pip install -e .
 ```
 This mode, allows to edit the source code and easily contribute back to the open-source project.
 
+In this mode  you can also install and run our end to end examples using:
+```bash
+$ pip install -e examples
+```
 An alternative, is to simply install using PyPI 
 ```bash
 $ pip install fuse-med-ml
 ```
 
- FuseMedML supports Python 3.6 or later and PyTorch 1.5 or later. A full list of dependencies can be found in [**requirements.txt**](https://github.com/IBM/fuse-med-ml/tree/master/requirements.txt).
+ FuseMedML supports Python 3.7 or later and PyTorch 1.5 or later. A full list of dependencies can be found in [**requirements.txt**](https://github.com/IBM/fuse-med-ml/tree/master/requirements.txt).
 
 
 # Ready to get started?
 ## FuseMedML from the ground up
 [**User Guide**](https://github.com/IBM/fuse-med-ml/tree/master/fuse/doc/user_guide.md) - including detailed explanation about FuseMedML modules, structure, concept, and more.
 
-[**Hello World**](https://colab.research.google.com/github/IBM/fuse-med-ml/blob/master/fuse_examples/tutorials/hello_world/hello_world.ipynb) - Introductory hands-on notebook on the well-known MNIST dataset. 
+[**Hello World**](https://colab.research.google.com/github/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/hello_world/hello_world.ipynb) - Introductory hands-on notebook on the well-known MNIST dataset. 
 
 [**High Level Code Example**](https://github.com/IBM/fuse-med-ml/tree/master/fuse/doc/high_level_example.md) - example of binary classifier for mammography with an auxiliary segmentation loss and clinical data
 
 ## Examples
 * classification
-    * [**MNIST**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/mnist/)  - a simple example, including training, inference and evaluation over [MNIST dataset](http://yann.lecun.com/exdb/mnist/)
-    * [**KNIGHT Challenge**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/knight) - preoperative prediction of risk class for patients with renal masses identified in clinical Computed Tomography (CT) imaging of the kidneys. Including data pre-processing, baseline implementation and evaluation pipeline for the challenge.
-    * [**Multimodality tutorial**](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb) - demonstration of two popular simple methods integrating imaging and clinical data (tabular) using FuseMedML  
-    * [**Skin Lesion**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/skin_lesion/) - skin lesion classification , including training, inference and evaluation over the public dataset introduced in [ISIC challenge](https://challenge.isic-archive.com/landing/2017)
-    * [**Prostate Gleason Classifiaction**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/prostate_x/) - lesions classification of Gleason score in prostate over the public dataset introduced in [SPIE-AAPM-NCI PROSTATEx challenge](https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges#23691656d4622c5ad5884bdb876d6d441994da38)
-    * [**Lesion Stage Classification**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/duke_breast_cancer/) - lesions classification of Tumor Stage (Size) in breast MRI over the public dataset introduced in [Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (Duke-Breast-Cancer-MRI)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903)
-    * [**Breast Cancer Lesion Classification**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/MG_CMMD) - lesions classification of tumor ( benign, malignant) in breast mammography over the public dataset introduced in [The Chinese Mammography Database (CMMD)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70230508)
+    * [**MNIST**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/mnist/)  - a simple example, including training, inference and evaluation over [MNIST dataset](http://yann.lecun.com/exdb/mnist/)
+    * [**KNIGHT Challenge**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/knight) - preoperative prediction of risk class for patients with renal masses identified in clinical Computed Tomography (CT) imaging of the kidneys. Including data pre-processing, baseline implementation and evaluation pipeline for the challenge.
+    * [**Multimodality tutorial**](https://github.com/IBM/fuse-med-ml/blob/master/examples/fuse_examples/multimodality/image_clinical/multimodality_image_clinical.ipynb) - demonstration of two popular simple methods integrating imaging and clinical data (tabular) using FuseMedML  
+    * [**Skin Lesion**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/skin_lesion/) - skin lesion classification , including training, inference and evaluation over the public dataset introduced in [ISIC challenge](https://challenge.isic-archive.com/landing/2017)
+    * [**Prostate Gleason Classification**](https://github.com/IBM/fuse-med-ml/tree/master/example/fuse_examples/imaging/classification/prostate_x/) - lesions classification of Gleason score in prostate over the public dataset introduced in [SPIE-AAPM-NCI PROSTATEx challenge](https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges#23691656d4622c5ad5884bdb876d6d441994da38)
+    * [**Lesion Stage Classification**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/duke_breast_cancer/) - lesions classification of Tumor Stage (Size) in breast MRI over the public dataset introduced in [Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (Duke-Breast-Cancer-MRI)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903)
+    * [**Breast Cancer Lesion Classification**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/MG_CMMD) - lesions classification of tumor ( benign, malignant) in breast mammography over the public dataset introduced in [The Chinese Mammography Database (CMMD)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70230508)
 
 ## Walkthrough template
 * [**Walkthrough Template**](https://github.com/IBM/fuse-med-ml/tree/master/fuse/templates/walkthrough_template.py) - includes several TODO notes, marking the minimal scope of code required to get your pipeline up and running. The template also includes useful explanations and tips.

diff --git a/VERSION.txt b/VERSION.txt
@@ -0,0 +1 @@
+0.2.0
diff --git a/fuse/data/augmentor/__init__.py → examples/fuse_examples/imaging/__init__.py b/fuse/data/augmentor/__init__.py → examples/fuse_examples/imaging/__init__.py
diff --git a/...examples/classification/MG_CMMD/README.md → .../imaging/classification/MG_CMMD/README.md b/...examples/classification/MG_CMMD/README.md → .../imaging/classification/MG_CMMD/README.md
diff --git a/fuse/data/cache/__init__.py → ...amples/imaging/classification/__init__.py b/fuse/data/cache/__init__.py → ...amples/imaging/classification/__init__.py
diff --git a/..._examples/classification/bright/README.md → ...s/imaging/classification/bright/README.md b/..._examples/classification/bright/README.md → ...s/imaging/classification/bright/README.md
@@ -42,50 +42,50 @@ The participants should submit a .csv file per task containing a row with a fina
 **Task 1 Prediction File:**
 \[image_name,predicted_label,Noncancerous-score,Precancerous-score,Cancerous-score\]
 
-See [example prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/example/example_task1_predictions.csv)
+See [example prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/example/example_task1_predictions.csv)
 
 **Task 2 Prediction File:**
 \[image_name,predicted_label,PB-score,UDH-score,FEA-score,ADH-score,DCIS-score,IC-score\]
 
-See [example prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/example/example_task2_predictions.csv)
+See [example prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/example/example_task2_predictions.csv)
 
 Where “image_name" represents the sample (e.g. BRACS_264) and all scores represent the probability of a patient to belong to a class.
 
-The evaluation script together with a dummy prediction files can be found in `fuse-med-ml/fuse_examples/classification/bright/eval`
+The evaluation script together with a dummy prediction files can be found in `fuse-med-ml/fuse_examples/imaging/classification/bright/eval`
 More details can be found in [challenge website](https://research.ibm.com/haifa/Workshops/BRIGHT)
 
 
 <br/>
 
 To run the evaluation script:
 ```
-cd fuse-med-ml/fuse_examples/classification/knight/eval
+cd fuse-med-ml/fuse_examples/imaging/classification/knight/eval
 python eval.py <target_filename> <task1 prediction_filename> <task1 prediction_filename> <output dir>
 ```
 To evaluate the dummy example predictions and targets
 ```
-cd fuse-med-ml/fuse_examples/classification/knight/eval 
+cd fuse-med-ml/fuse_examples/imaging/classification/knight/eval 
 python eval.py example/example_targets.csv example/example_task1_predictions.csv example/example_task2_predictions.csv example/results
 ```    
 
 ### Baseline
 As an additional example, we also include the validation prediction files and validation target file of the challenge baseline implementation:
 
 
-See [validation baseline prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/baseline/validation_baseline_task1_predictions.csv)
+See [validation baseline prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/baseline/validation_baseline_task1_predictions.csv)
 
 
-See [validation baseline prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/baseline/validation_baseline_task2_predictions.csv)
+See [validation baseline prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/baseline/validation_baseline_task2_predictions.csv)
 
 
-See [validation targets file](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/validation_targets.csv)
+See [validation targets file](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/validation_targets.csv)
 
 
 
 <br/>
 
 To evaluate the baseline predictions over the validation set:
 ```
-cd fuse-med-ml/fuse_examples/classification/bright/eval
+cd fuse-med-ml/fuse_examples/imaging.classification/bright/eval
 python eval.py validation_targets.csv baseline/validation_baseline_task1_predictions.csv baseline/validation_baseline_task2_predictions.csv baseline/validation_results
 ```
diff --git a/fuse/data/data_source/__init__.py → ...ng/classification/bright/eval/__init__.py b/fuse/data/data_source/__init__.py → ...ng/classification/bright/eval/__init__.py
diff --git a/...validation_baseline_task1_predictions.csv → ...validation_baseline_task1_predictions.csv b/...validation_baseline_task1_predictions.csv → ...validation_baseline_task1_predictions.csv
diff --git a/...validation_baseline_task2_predictions.csv → ...validation_baseline_task2_predictions.csv b/...validation_baseline_task2_predictions.csv → ...validation_baseline_task2_predictions.csv
diff --git a/...l/baseline/validation_results/results.csv → ...l/baseline/validation_results/results.csv b/...l/baseline/validation_results/results.csv → ...l/baseline/validation_results/results.csv
diff --git a/...al/baseline/validation_results/results.md → ...al/baseline/validation_results/results.md b/...al/baseline/validation_results/results.md → ...al/baseline/validation_results/results.md
diff --git a/...amples/classification/bright/eval/eval.py → ...maging/classification/bright/eval/eval.py b/...amples/classification/bright/eval/eval.py → ...maging/classification/bright/eval/eval.py
@@ -207,8 +207,8 @@ def eval(task1_prediction_filename: str, task2_prediction_filename: str, target_
     """
     Run evaluation:
     Usage: python eval.py <target_filename> <task1_prediction_filename> <task2_prediction_filename> <output dir>
-    Run dummy example (set the working dir to fuse-med-ml/fuse_examples/classification/bright/eval): python eval.py example/example_targets.csv example/example_task1_predictions.csv example/example_task2_predictions.csv example/results
-    Run baseline (set the working dir to fuse-med-ml/fuse_examples/classification/bright/eval): python eval.py validation_targets.csv baseline/validation_baseline_task1_predictions.csv baseline/validation_baseline_task2_predictions.csv baseline/validation_results
+    Run dummy example (set the working dir to fuse-med-ml/fuse_examples/imaging/classification/bright/eval): python eval.py example/example_targets.csv example/example_task1_predictions.csv example/example_task2_predictions.csv example/results
+    Run baseline (set the working dir to fuse-med-ml/fuse_examples/imaging/classification/bright/eval): python eval.py validation_targets.csv baseline/validation_baseline_task1_predictions.csv baseline/validation_baseline_task2_predictions.csv baseline/validation_results
     """
 
     if len(sys.argv) == 1:

diff --git a/...n/bright/eval/example/example_targets.csv → ...n/bright/eval/example/example_targets.csv b/...n/bright/eval/example/example_targets.csv → ...n/bright/eval/example/example_targets.csv
diff --git a/...val/example/example_task1_predictions.csv → ...val/example/example_task1_predictions.csv b/...val/example/example_task1_predictions.csv → ...val/example/example_task1_predictions.csv
diff --git a/...val/example/example_task2_predictions.csv → ...val/example/example_task2_predictions.csv b/...val/example/example_task2_predictions.csv → ...val/example/example_task2_predictions.csv
diff --git a/...n/bright/eval/example/results/results.csv → ...n/bright/eval/example/results/results.csv b/...n/bright/eval/example/results/results.csv → ...n/bright/eval/example/results/results.csv
diff --git a/...on/bright/eval/example/results/results.md → ...on/bright/eval/example/results/results.md b/...on/bright/eval/example/results/results.md → ...on/bright/eval/example/results/results.md
diff --git a/...cation/bright/eval/validation_targets.csv → ...cation/bright/eval/validation_targets.csv b/...cation/bright/eval/validation_targets.csv → ...cation/bright/eval/validation_targets.csv
diff --git a/fuse_examples/classification/cmmd/dataset.py → ...es/imaging/classification/cmmd/dataset.py b/fuse_examples/classification/cmmd/dataset.py → ...es/imaging/classification/cmmd/dataset.py
@@ -7,30 +7,30 @@
 from pathlib import Path
 
 
-from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault
-from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault
+from fuse.data.visualizer.visualizer_default import VisualizerDefault
+from fuse.data.augmentor.augmentor_default import AugmentorDefault
 from fuse.data.augmentor.augmentor_toolbox import aug_op_color, aug_op_gaussian, aug_op_affine
-from fuse.data.dataset.dataset_default import FuseDatasetDefault
+from fuse.data.dataset.dataset_default import DatasetDefault
 
 from fuse.utils.rand.param_sampler import Uniform, RandInt, RandBool
 
-from fuse_examples.classification.cmmd.input_processor import FuseMGInputProcessor
-from fuse_examples.classification.cmmd.ground_truth_processor import FuseMGGroundTruthProcessor
-from fuse.data.data_source.data_source_folds import FuseDataSourceFolds
+from fuse_examples.imaging.classification.cmmd.input_processor import MGInputProcessor
+from fuse_examples.imaging.classification.cmmd.ground_truth_processor import MGGroundTruthProcessor
+from fuse.data.data_source.data_source_folds import DataSourceFolds
 
 from typing import Tuple
 
 
 def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache', reset_cache: bool = False,
-                      post_cache_processing_func: Optional[Callable] = None) -> Tuple[FuseDatasetDefault, FuseDatasetDefault]:
+                      post_cache_processing_func: Optional[Callable] = None) -> Tuple[DatasetDefault, DatasetDefault]:
     """
     Creates Fuse Dataset object for training, validation and test
     :param data_dir:                    dataset root path
     :param data_misc_dir                path to save misc files to be used later
     :param cache_dir:                   Optional, name of the cache folder
     :param reset_cache:                 Optional,specifies if we want to clear the cache first
     :param post_cache_processing_func:  Optional, function run post cache processing
-    :return: training, validation and test FuseDatasetDefault objects
+    :return: training, validation and test DatasetDefault objects
     """
     augmentation_pipeline = [
         [
@@ -60,7 +60,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache
     input_source_gt = merge_clinical_data_with_dicom_tags(data_dir, data_misc_dir, target)
 
     partition_file_path = os.path.join(data_misc_dir, 'data_fold_new.csv')
-    train_data_source = FuseDataSourceFolds(input_source=input_source_gt,
+    train_data_source = DataSourceFolds(input_source=input_source_gt,
                                             input_df=None,
                                             phase='train',
                                             no_mixture_id='ID1',
@@ -73,21 +73,21 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache
 
     # Create data processors:
     input_processors = {
-        'image': FuseMGInputProcessor(input_data=data_dir)
+        'image': MGInputProcessor(input_data=data_dir)
     }
     gt_processors = {
-        'classification': FuseMGGroundTruthProcessor(input_data=input_source_gt)
+        'classification': MGGroundTruthProcessor(input_data=input_source_gt)
     }
 
     # Create data augmentation (optional)
-    augmentor = FuseAugmentorDefault(
+    augmentor = AugmentorDefault(
         augmentation_pipeline=augmentation_pipeline)
 
     # Create visualizer (optional)
-    visualiser = FuseVisualizerDefault(image_name='data.input.image', label_name='data.gt.classification')
+    visualiser = VisualizerDefault(image_name='data.input.image', label_name='data.gt.classification')
 
     # Create train dataset
-    train_dataset = FuseDatasetDefault(cache_dest=cache_dir,
+    train_dataset = DatasetDefault(cache_dest=cache_dir,
                                        data_source=train_data_source,
                                        input_processors=input_processors,
                                        gt_processors=gt_processors,
@@ -100,7 +100,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache
     lgr.info(f'- Load and cache data: Done')
 
     # Create validation data source
-    validation_data_source = FuseDataSourceFolds(input_source=input_source_gt,
+    validation_data_source = DataSourceFolds(input_source=input_source_gt,
                                             input_df=None,
                                             phase='validation',
                                             no_mixture_id='ID1',
@@ -111,7 +111,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache
                                             partition_file_name=partition_file_path)
 
     ## Create dataset
-    validation_dataset = FuseDatasetDefault(cache_dest=cache_dir,
+    validation_dataset = DatasetDefault(cache_dest=cache_dir,
                                             data_source=validation_data_source,
                                             input_processors=input_processors,
                                             gt_processors=gt_processors,
@@ -120,7 +120,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache
                                             visualizer=visualiser)
     validation_dataset.create( pool_type='thread')  # use ThreadPool to create this dataset, to avoid cv2 problems in multithreading
 
-    test_data_source =  FuseDataSourceFolds(input_source=input_source_gt,
+    test_data_source =  DataSourceFolds(input_source=input_source_gt,
                                             input_df=None,
                                             phase='test',
                                             no_mixture_id='ID1',
@@ -129,7 +129,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache
                                             folds=[4],
                                             num_folds=5,
                                             partition_file_name=partition_file_path)
-    test_dataset = FuseDatasetDefault(cache_dest=cache_dir,
+    test_dataset = DatasetDefault(cache_dest=cache_dir,
                                             data_source=test_data_source,
                                             input_processors=input_processors,
                                             gt_processors=gt_processors,

diff --git a/...sification/cmmd/ground_truth_processor.py → ...sification/cmmd/ground_truth_processor.py b/...sification/cmmd/ground_truth_processor.py → ...sification/cmmd/ground_truth_processor.py
@@ -23,10 +23,10 @@
 import pandas as pd
 import numpy as np
 
-from fuse.data.processor.processor_base import FuseProcessorBase
+from fuse.data.processor.processor_base import ProcessorBase
 
 
-class FuseMGGroundTruthProcessor(FuseProcessorBase):
+class MGGroundTruthProcessor(ProcessorBase):
     def __init__(self,
                  input_data: str):