Add more figures

Author: manthey

paper/paper.bib

@@ -55,3 +55,11 @@ @article{Gutman2017
   month = oct,
   pages = {e75–e78}
 }
+
+@misc{TCGAData,
+  author = {National Cancer Institute and National Human Genome Research Institute},
+  title = {The Cancer Genome Atlas (TCGA) Program},
+  year = {2022},
+  url = {https://www.cancer.gov/tcga},
+  note = {Accessed: 2022-11-10]}
+}

paper/paper.md

@@ -62,8 +62,22 @@ For development, the initial segmentation uses superpixels generated with the SL
 
 We had a placental pathologist provide feedback to validate the efficiency of the user interface and utility of the process.
 
+# Basic Workflow
+
+When starting a new labeling project, the user selects how superpixels are generated, which certainty metric is used for determining the optimal labeling order, and what features are used for model training.  The labeling mode allows defining project labels and performing initial labeling.  This mode can also be used to add new label categories or combine two categories if they should not have been distinct.
+
+![The Bulk Labeling interface showing one of the project images divided into superpixels with some categories defined.  A user can "paint" areas with known labels as an initial seed for the guided labeling process](../docs/screenshots/initial_labels.png)
+
+Once some segments have been labeled and an initial training process has been performed, additional segments are shown with their predictions.  The user can use keyboard shortcuts or the mouse to confirm or correct labels.  These are presented in an order that maximizes the utility of improving the model based on the originally selected certainty metric.
+
 ![The Guided Labeling interface showing a row of superpixels to be labeled and part of a whole slide image](../docs/screenshots/active_learning_view.png)
 
+To check on overall behavior or correct mistakes, there is a review mode that allows seeing all labeled segments with various filtering and sorting options.  This can be used to check agreement between pathologists or determine how well the model agrees with the manually labeled data.
+
+![The Review interface showing labeled superpixels in each category](../docs/screenshots/reviewmode.png)
+
+The whole slide image data in these figures are from data generated by the TCGA Research Network [@TCGA].
+
 # Acknowledgements
 
 This work has been funded in part by National Library of Medicine grant 5R01LM013523 entitled "Guiding humans to create better labeled datasets for machine learning in biomedical research".